CINXE.COM

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: quantile regression</title> <meta name="description" content="Search results for: quantile regression"> <meta name="keywords" content="quantile regression"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="quantile regression" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="quantile regression"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3239</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: quantile regression</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3239</span> Integrated Nested Laplace Approximations For Quantile Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kajingulu%20Malandala">Kajingulu Malandala</a>, <a href="https://publications.waset.org/abstracts/search?q=Ranganai%20Edmore"> Ranganai Edmore</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The asymmetric Laplace distribution (ADL) is commonly used as the likelihood function of the Bayesian quantile regression, and it offers different families of likelihood method for quantile regression. Notwithstanding their popularity and practicality, ADL is not smooth and thus making it difficult to maximize its likelihood. Furthermore, Bayesian inference is time consuming and the selection of likelihood may mislead the inference, as the Bayes theorem does not automatically establish the posterior inference. Furthermore, ADL does not account for greater skewness and Kurtosis. This paper develops a new aspect of quantile regression approach for count data based on inverse of the cumulative density function of the Poisson, binomial and Delaporte distributions using the integrated nested Laplace Approximations. Our result validates the benefit of using the integrated nested Laplace Approximations and support the approach for count data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression" title="quantile regression">quantile regression</a>, <a href="https://publications.waset.org/abstracts/search?q=Delaporte%20distribution" title=" Delaporte distribution"> Delaporte distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=count%20data" title=" count data"> count data</a>, <a href="https://publications.waset.org/abstracts/search?q=integrated%20nested%20Laplace%20approximation" title=" integrated nested Laplace approximation"> integrated nested Laplace approximation</a> </p> <a href="https://publications.waset.org/abstracts/123306/integrated-nested-laplace-approximations-for-quantile-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123306.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">163</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3238</span> Urban-Rural Inequality in Mexico after Nafta: A Quantile Regression Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rene%20Valdiviezo-Issa">Rene Valdiviezo-Issa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we use Mexico’s Households Income and Expenditures (ENIGH) survey to explain the behaviour that the urban-rural expenditure gap has had since Mexico’s incorporation to the North American Free Trade Agreement (NAFTA) in 1994 and we compare it with the latest available survey, which took place in 2014. We use real trimestral expenditure per capita (RTEPC) as the measure of welfare. We use quantile regressions and a quantile regression decomposition to describe the gap between urban and rural distributions of log RTEPC. We discover that the decrease in the difference between the urban and rural distributions of log RTEPC, or inequality, is motivated because of a deprivation of the urban areas, in very specific characteristics, rather than an improvement of the urban areas. When using the decomposition we observe that the gap is primarily brought about because differences in returns to covariates between the urban and rural areas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression" title="quantile regression">quantile regression</a>, <a href="https://publications.waset.org/abstracts/search?q=urban-rural%20inequality" title=" urban-rural inequality"> urban-rural inequality</a>, <a href="https://publications.waset.org/abstracts/search?q=inequality%20in%20Mexico" title=" inequality in Mexico"> inequality in Mexico</a>, <a href="https://publications.waset.org/abstracts/search?q=income%20decompositon" title=" income decompositon"> income decompositon</a> </p> <a href="https://publications.waset.org/abstracts/43665/urban-rural-inequality-in-mexico-after-nafta-a-quantile-regression-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43665.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">282</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3237</span> Quantile Smoothing Splines: Application on Productivity of Enterprises</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Semra%20Turkan">Semra Turkan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have examined the factors that affect the productivity of Turkey’s Top 500 Industrial Enterprises in 2014. The labor productivity of enterprises is taken as an indicator of productivity of industrial enterprises. When the relationships between some financial ratios and labor productivity, it is seen that there is a nonparametric relationship between labor productivity and return on sales. In addition, the distribution of labor productivity of enterprises is right-skewed. If the dependent distribution is skewed, the quantile regression is more suitable for this data. Hence, the nonparametric relationship between labor productivity and return on sales by quantile smoothing splines. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression" title="quantile regression">quantile regression</a>, <a href="https://publications.waset.org/abstracts/search?q=smoothing%20spline" title=" smoothing spline"> smoothing spline</a>, <a href="https://publications.waset.org/abstracts/search?q=labor%20productivity" title=" labor productivity"> labor productivity</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20ratios" title=" financial ratios"> financial ratios</a> </p> <a href="https://publications.waset.org/abstracts/60552/quantile-smoothing-splines-application-on-productivity-of-enterprises" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60552.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">302</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3236</span> Efficient Model Selection in Linear and Non-Linear Quantile Regression by Cross-Validation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yoonsuh%20Jung">Yoonsuh Jung</a>, <a href="https://publications.waset.org/abstracts/search?q=Steven%20N.%20MacEachern"> Steven N. MacEachern</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Check loss function is used to define quantile regression. In the prospect of cross validation, it is also employed as a validation function when underlying truth is unknown. However, our empirical study indicates that the validation with check loss often leads to choosing an over estimated fits. In this work, we suggest a modified or L2-adjusted check loss which rounds the sharp corner in the middle of check loss. It has a large effect of guarding against over fitted model in some extent. Through various simulation settings of linear and non-linear regressions, the improvement of check loss by L2 adjustment is empirically examined. This adjustment is devised to shrink to zero as sample size grows. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cross-validation" title="cross-validation">cross-validation</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20selection" title=" model selection"> model selection</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression" title=" quantile regression"> quantile regression</a>, <a href="https://publications.waset.org/abstracts/search?q=tuning%20parameter%20selection" title=" tuning parameter selection"> tuning parameter selection</a> </p> <a href="https://publications.waset.org/abstracts/44203/efficient-model-selection-in-linear-and-non-linear-quantile-regression-by-cross-validation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44203.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">438</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3235</span> Currency Exchange Rate Forecasts Using Quantile Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuzhi%20Cai">Yuzhi Cai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we discuss a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. Together with a combining forecasts technique, we then predict USD to GBP currency exchange rates. Combined forecasts contain all the information captured by the fitted QAR models at different quantile levels and are therefore better than those obtained from individual models. Our results show that an unequally weighted combining method performs better than other forecasting methodology. We found that a median AR model can perform well in point forecasting when the predictive density functions are symmetric. However, in practice, using the median AR model alone may involve the loss of information about the data captured by other QAR models. We recommend that combined forecasts should be used whenever possible. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=combining%20forecasts" title="combining forecasts">combining forecasts</a>, <a href="https://publications.waset.org/abstracts/search?q=MCMC" title=" MCMC"> MCMC</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20density%20functions" title=" predictive density functions"> predictive density functions</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20forecasting" title=" quantile forecasting"> quantile forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20modelling" title=" quantile modelling"> quantile modelling</a> </p> <a href="https://publications.waset.org/abstracts/45531/currency-exchange-rate-forecasts-using-quantile-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45531.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">256</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3234</span> The Profit Trend of Cosmetics Products Using Bootstrap Edgeworth Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Edlira%20Donefski">Edlira Donefski</a>, <a href="https://publications.waset.org/abstracts/search?q=Lorenc%20Ekonomi"> Lorenc Ekonomi</a>, <a href="https://publications.waset.org/abstracts/search?q=Tina%20Donefski"> Tina Donefski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Edgeworth approximation is one of the most important statistical methods that has a considered contribution in the reduction of the sum of standard deviation of the independent variables’ coefficients in a Quantile Regression Model. This model estimates the conditional median or other quantiles. In this paper, we have applied approximating statistical methods in an economical problem. We have created and generated a quantile regression model to see how the profit gained is connected with the realized sales of the cosmetic products in a real data, taken from a local business. The Linear Regression of the generated profit and the realized sales was not free of autocorrelation and heteroscedasticity, so this is the reason that we have used this model instead of Linear Regression. Our aim is to analyze in more details the relation between the variables taken into study: the profit and the finalized sales and how to minimize the standard errors of the independent variable involved in this study, the level of realized sales. The statistical methods that we have applied in our work are Edgeworth Approximation for Independent and Identical distributed (IID) cases, Bootstrap version of the Model and the Edgeworth approximation for Bootstrap Quantile Regression Model. The graphics and the results that we have presented here identify the best approximating model of our study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bootstrap" title="bootstrap">bootstrap</a>, <a href="https://publications.waset.org/abstracts/search?q=edgeworth%20approximation" title=" edgeworth approximation"> edgeworth approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=IID" title=" IID"> IID</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile" title=" quantile"> quantile</a> </p> <a href="https://publications.waset.org/abstracts/135144/the-profit-trend-of-cosmetics-products-using-bootstrap-edgeworth-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135144.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">159</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3233</span> Bayesian Variable Selection in Quantile Regression with Application to the Health and Retirement Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Priya%20Kedia">Priya Kedia</a>, <a href="https://publications.waset.org/abstracts/search?q=Kiranmoy%20Das"> Kiranmoy Das</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There is a rich literature on variable selection in regression setting. However, most of these methods assume normality for the response variable under consideration for implementing the methodology and establishing the statistical properties of the estimates. In many real applications, the distribution for the response variable may be non-Gaussian, and one might be interested in finding the best subset of covariates at some predetermined quantile level. We develop dynamic Bayesian approach for variable selection in quantile regression framework. We use a zero-inflated mixture prior for the regression coefficients, and consider the asymmetric Laplace distribution for the response variable for modeling different quantiles of its distribution. An efficient Gibbs sampler is developed for our computation. Our proposed approach is assessed through extensive simulation studies, and real application of the proposed approach is also illustrated. We consider the data from health and retirement study conducted by the University of Michigan, and select the important predictors when the outcome of interest is out-of-pocket medical cost, which is considered as an important measure for financial risk. Our analysis finds important predictors at different quantiles of the outcome, and thus enhance our understanding on the effects of different predictors on the out-of-pocket medical cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=variable%20selection" title="variable selection">variable selection</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression" title=" quantile regression"> quantile regression</a>, <a href="https://publications.waset.org/abstracts/search?q=Gibbs%20sampler" title=" Gibbs sampler"> Gibbs sampler</a>, <a href="https://publications.waset.org/abstracts/search?q=asymmetric%20Laplace%20distribution" title=" asymmetric Laplace distribution"> asymmetric Laplace distribution</a> </p> <a href="https://publications.waset.org/abstracts/122459/bayesian-variable-selection-in-quantile-regression-with-application-to-the-health-and-retirement-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122459.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">156</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3232</span> The Impact of Governance on Happiness: Evidence from Quantile Regressions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chiung-Ju%20Huang">Chiung-Ju Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study utilizes the quantile regression analysis to examine the impact of governance (including democratic quality and technical quality) on happiness in 101 countries worldwide, classified as “developed countries” and “developing countries”. The empirical results show that the impact of democratic quality and technical quality on happiness is significantly positive for “developed countries”, while is insignificant for “developing countries”. The results suggest that the authorities in developed countries can enhance the level of individual happiness by means of improving the democracy quality and technical quality. However, for developing countries, promoting the quality of governance in order to enhance the level of happiness may not be effective. Policy makers in developed countries may pay more attention on increasing real GDP per capita instead of promoting the quality of governance to enhance individual happiness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=governance" title="governance">governance</a>, <a href="https://publications.waset.org/abstracts/search?q=happiness" title=" happiness"> happiness</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20regression" title=" multiple regression"> multiple regression</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression" title=" quantile regression"> quantile regression</a> </p> <a href="https://publications.waset.org/abstracts/53398/the-impact-of-governance-on-happiness-evidence-from-quantile-regressions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53398.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">281</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3231</span> Application of the Quantile Regression Approach to the Heterogeneity of the Fine Wine Prices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Charles-Olivier%20Am%C3%A9d%C3%A9e-Manesme">Charles-Olivier Amédée-Manesme</a>, <a href="https://publications.waset.org/abstracts/search?q=Benoit%20Faye"> Benoit Faye</a>, <a href="https://publications.waset.org/abstracts/search?q=Eric%20Le%20Fur"> Eric Le Fur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the heterogeneity of the Bordeaux Legends 50 wine market price segment is addressed. For this purpose, quantile regression is applied – with market segmentation based on wine bottle price quantile – and the hedonic price of wine attributes is computed for various price segments of the market. The approach is applied to a major privately held data set which consists of approximately 30,000 transactions over the 2003–2014 period. The findings suggest that the relative hedonic prices of several wine attributes differ significantly among deciles. In particular, the elasticity coefficient of the expert ratings shows strong variation among prices. If - as suggested in the literature - expert ratings have a positive influence on wine price on average, they have a clearly decreasing impact over the quantiles. Finally, the lower the wine price, the higher the potential for price appreciation over time. Other variables such as chateaux or vintage are also shown to vary across the distribution of wine prices. While enhancing our understanding of the complex market dynamics that underlie Bordeaux wines’ price, this research provides empirical evidence that the QR approach adequately captures heterogeneity among wine price ranges, which simultaneously applies to wine stock, vintage and auctions’ house. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hedonics" title="hedonics">hedonics</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20segmentation" title=" market segmentation"> market segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression" title=" quantile regression"> quantile regression</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneity" title=" heterogeneity"> heterogeneity</a>, <a href="https://publications.waset.org/abstracts/search?q=wine%20economics" title=" wine economics"> wine economics</a> </p> <a href="https://publications.waset.org/abstracts/70068/application-of-the-quantile-regression-approach-to-the-heterogeneity-of-the-fine-wine-prices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70068.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">340</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3230</span> Enhancing the Interpretation of Group-Level Diagnostic Results from Cognitive Diagnostic Assessment: Application of Quantile Regression and Cluster Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wenbo%20Du">Wenbo Du</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaomei%20Ma"> Xiaomei Ma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the empowerment of Cognitive Diagnostic Assessment (CDA), various domains of language testing and assessment have been investigated to dig out more diagnostic information. What is noticeable is that most of the extant empirical CDA-based research puts much emphasis on individual-level diagnostic purpose with very few concerned about learners’ group-level performance. Even though the personalized diagnostic feedback is the unique feature that differentiates CDA from other assessment tools, group-level diagnostic information cannot be overlooked in that it might be more practical in classroom setting. Additionally, the group-level diagnostic information obtained via current CDA always results in a “flat pattern”, that is, the mastery/non-mastery of all tested skills accounts for the two highest proportion. In that case, the outcome does not bring too much benefits than the original total score. To address these issues, the present study attempts to apply cluster analysis for group classification and quantile regression analysis to pinpoint learners’ performance at different proficiency levels (beginner, intermediate and advanced) thus to enhance the interpretation of the CDA results extracted from a group of EFL learners’ reading performance on a diagnostic reading test designed by PELDiaG research team from a key university in China. The results show that EM method in cluster analysis yield more appropriate classification results than that of CDA, and quantile regression analysis does picture more insightful characteristics of learners with different reading proficiencies. The findings are helpful and practical for instructors to refine EFL reading curriculum and instructional plan tailored based on the group classification results and quantile regression analysis. Meanwhile, these innovative statistical methods could also make up the deficiencies of CDA and push forward the development of language testing and assessment in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20diagnostic%20assessment" title="cognitive diagnostic assessment">cognitive diagnostic assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnostic%20feedback" title=" diagnostic feedback"> diagnostic feedback</a>, <a href="https://publications.waset.org/abstracts/search?q=EFL%20reading" title=" EFL reading"> EFL reading</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression" title=" quantile regression"> quantile regression</a> </p> <a href="https://publications.waset.org/abstracts/132566/enhancing-the-interpretation-of-group-level-diagnostic-results-from-cognitive-diagnostic-assessment-application-of-quantile-regression-and-cluster-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132566.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3229</span> Nonparametric Quantile Regression for Multivariate Spatial Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20H.%20Arnaud%20Kanga">S. H. Arnaud Kanga</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20Hili"> O. Hili</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Dabo-Niang"> S. Dabo-Niang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spatial prediction is an issue appealing and attracting several fields such as agriculture, environmental sciences, ecology, econometrics, and many others. Although multiple non-parametric prediction methods exist for spatial data, those are based on the conditional expectation. This paper took a different approach by examining a non-parametric spatial predictor of the conditional quantile. The study especially observes the stationary multidimensional spatial process over a rectangular domain. Indeed, the proposed quantile is obtained by inverting the conditional distribution function. Furthermore, the proposed estimator of the conditional distribution function depends on three kernels, where one of them controls the distance between spatial locations, while the other two control the distance between observations. In addition, the almost complete convergence and the convergence in mean order q of the kernel predictor are obtained when the sample considered is alpha-mixing. Such approach of the prediction method gives the advantage of accuracy as it overcomes sensitivity to extreme and outliers values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conditional%20quantile" title="conditional quantile">conditional quantile</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel" title=" kernel"> kernel</a>, <a href="https://publications.waset.org/abstracts/search?q=nonparametric" title=" nonparametric"> nonparametric</a>, <a href="https://publications.waset.org/abstracts/search?q=stationary" title=" stationary"> stationary</a> </p> <a href="https://publications.waset.org/abstracts/109937/nonparametric-quantile-regression-for-multivariate-spatial-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109937.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3228</span> Bayesian Value at Risk Forecast Using Realized Conditional Autoregressive Expectiel Mdodel with an Application of Cryptocurrency</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Niya%20Chen">Niya Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Jennifer%20Chan"> Jennifer Chan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the financial market, risk management helps to minimize potential loss and maximize profit. There are two ways to assess risks; the first way is to calculate the risk directly based on the volatility. The most common risk measurements are Value at Risk (VaR), sharp ratio, and beta. Alternatively, we could look at the quantile of the return to assess the risk. Popular return models such as GARCH and stochastic volatility (SV) focus on modeling the mean of the return distribution via capturing the volatility dynamics; however, the quantile/expectile method will give us an idea of the distribution with the extreme return value. It will allow us to forecast VaR using return which is direct information. The advantage of using these non-parametric methods is that it is not bounded by the distribution assumptions from the parametric method. But the difference between them is that expectile uses a second-order loss function while quantile regression uses a first-order loss function. We consider several quantile functions, different volatility measures, and estimates from some volatility models. To estimate the expectile of the model, we use Realized Conditional Autoregressive Expectile (CARE) model with the bayesian method to achieve this. We would like to see if our proposed models outperform existing models in cryptocurrency, and we will test it by using Bitcoin mainly as well as Ethereum. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=expectile" title="expectile">expectile</a>, <a href="https://publications.waset.org/abstracts/search?q=CARE%20Model" title=" CARE Model"> CARE Model</a>, <a href="https://publications.waset.org/abstracts/search?q=CARR%20Model" title=" CARR Model"> CARR Model</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile" title=" quantile"> quantile</a>, <a href="https://publications.waset.org/abstracts/search?q=cryptocurrency" title=" cryptocurrency"> cryptocurrency</a>, <a href="https://publications.waset.org/abstracts/search?q=Value%20at%20Risk" title=" Value at Risk"> Value at Risk</a> </p> <a href="https://publications.waset.org/abstracts/159362/bayesian-value-at-risk-forecast-using-realized-conditional-autoregressive-expectiel-mdodel-with-an-application-of-cryptocurrency" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159362.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">109</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3227</span> Selection of Designs in Ordinal Regression Models under Linear Predictor Misspecification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ishapathik%20Das">Ishapathik Das</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this article is to find a method of comparing designs for ordinal regression models using quantile dispersion graphs in the presence of linear predictor misspecification. The true relationship between response variable and the corresponding control variables are usually unknown. Experimenter assumes certain form of the linear predictor of the ordinal regression models. The assumed form of the linear predictor may not be correct always. Thus, the maximum likelihood estimates (MLE) of the unknown parameters of the model may be biased due to misspecification of the linear predictor. In this article, the uncertainty in the linear predictor is represented by an unknown function. An algorithm is provided to estimate the unknown function at the design points where observations are available. The unknown function is estimated at all points in the design region using multivariate parametric kriging. The comparison of the designs are based on a scalar valued function of the mean squared error of prediction (MSEP) matrix, which incorporates both variance and bias of the prediction caused by the misspecification in the linear predictor. The designs are compared using quantile dispersion graphs approach. The graphs also visually depict the robustness of the designs on the changes in the parameter values. Numerical examples are presented to illustrate the proposed methodology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20misspecification" title="model misspecification">model misspecification</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20kriging" title=" multivariate kriging"> multivariate kriging</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20logistic%20link" title=" multivariate logistic link"> multivariate logistic link</a>, <a href="https://publications.waset.org/abstracts/search?q=ordinal%20response%20models" title=" ordinal response models"> ordinal response models</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20dispersion%20graphs" title=" quantile dispersion graphs"> quantile dispersion graphs</a> </p> <a href="https://publications.waset.org/abstracts/34042/selection-of-designs-in-ordinal-regression-models-under-linear-predictor-misspecification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34042.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">393</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3226</span> Quantile Coherence Analysis: Application to Precipitation Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yaeji%20Lim">Yaeji Lim</a>, <a href="https://publications.waset.org/abstracts/search?q=Hee-Seok%20Oh"> Hee-Seok Oh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The coherence analysis measures the linear time-invariant relationship between two data sets and has been studied various fields such as signal processing, engineering, and medical science. However classical coherence analysis tends to be sensitive to outliers and focuses only on mean relationship. In this paper, we generalized cross periodogram to quantile cross periodogram and provide richer inter-relationship between two data sets. This is a general version of Laplace cross periodogram. We prove its asymptotic distribution under the long range process and compare them with ordinary coherence through numerical examples. We also present real data example to confirm the usefulness of quantile coherence analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coherence" title="coherence">coherence</a>, <a href="https://publications.waset.org/abstracts/search?q=cross%20periodogram" title=" cross periodogram"> cross periodogram</a>, <a href="https://publications.waset.org/abstracts/search?q=spectrum" title=" spectrum"> spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile" title=" quantile"> quantile</a> </p> <a href="https://publications.waset.org/abstracts/42812/quantile-coherence-analysis-application-to-precipitation-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42812.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">390</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3225</span> The Effect of Accounting Conservatism on Cost of Capital: A Quantile Regression Approach for MENA Countries</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maha%20Zouaoui%20Khalifa">Maha Zouaoui Khalifa</a>, <a href="https://publications.waset.org/abstracts/search?q=Hakim%20Ben%20Othman"> Hakim Ben Othman</a>, <a href="https://publications.waset.org/abstracts/search?q=Hussaney%20Khaled"> Hussaney Khaled</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Prior empirical studies have investigated the economic consequences of accounting conservatism by examining its impact on the cost of equity capital (COEC). However, findings are not conclusive. We assume that inconsistent results of such association may be attributed to the regression models used in data analysis. To address this issue, we re-examine the effect of different dimension of accounting conservatism: unconditional conservatism (U_CONS) and conditional conservatism (C_CONS) on the COEC for a sample of listed firms from Middle Eastern and North Africa (MENA) countries, applying quantile regression (QR) approach developed by Koenker and Basset (1978). While classical ordinary least square (OLS) method is widely used in empirical accounting research, however it may produce inefficient and bias estimates in the case of departures from normality or long tail error distribution. QR method is more powerful than OLS to handle this kind of problem. It allows the coefficient on the independent variables to shift across the distribution of the dependent variable whereas OLS method only estimates the conditional mean effects of a response variable. We find as predicted that U_CONS has a significant positive effect on the COEC however, C_CONS has a negative impact. Findings suggest also that the effect of the two dimensions of accounting conservatism differs considerably across COEC quantiles. Comparing results from QR method with those of OLS, this study throws more lights on the association between accounting conservatism and COEC. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=unconditional%20conservatism" title="unconditional conservatism">unconditional conservatism</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20conservatism" title=" conditional conservatism"> conditional conservatism</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20of%20equity%20capital" title=" cost of equity capital"> cost of equity capital</a>, <a href="https://publications.waset.org/abstracts/search?q=OLS" title=" OLS"> OLS</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression" title=" quantile regression"> quantile regression</a>, <a href="https://publications.waset.org/abstracts/search?q=emerging%20markets" title=" emerging markets"> emerging markets</a>, <a href="https://publications.waset.org/abstracts/search?q=MENA%20countries" title=" MENA countries"> MENA countries</a> </p> <a href="https://publications.waset.org/abstracts/18733/the-effect-of-accounting-conservatism-on-cost-of-capital-a-quantile-regression-approach-for-mena-countries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18733.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">355</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3224</span> The Impact of Unconditional and Conditional Conservatism on Cost of Equity Capital: A Quantile Regression Approach for MENA Countries</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khalifa%20Maha">Khalifa Maha</a>, <a href="https://publications.waset.org/abstracts/search?q=Ben%20Othman%20Hakim"> Ben Othman Hakim</a>, <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Hussainey"> Khaled Hussainey </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Prior empirical studies have investigated the economic consequences of accounting conservatism by examining its impact on the cost of equity capital (COEC). However, findings are not conclusive. We assume that inconsistent results of such association may be attributed to the regression models used in data analysis. To address this issue, we re-examine the effect of different dimension of accounting conservatism: unconditional conservatism (U_CONS) and conditional conservatism (C_CONS) on the COEC for a sample of listed firms from Middle Eastern and North Africa (MENA) countries, applying quantile regression (QR) approach developed by Koenker and Basset (1978). While classical ordinary least square (OLS) method is widely used in empirical accounting research, however it may produce inefficient and bias estimates in the case of departures from normality or long tail error distribution. QR method is more powerful than OLS to handle this kind of problem. It allows the coefficient on the independent variables to shift across the distribution of the dependent variable whereas OLS method only estimates the conditional mean effects of a response variable. We find as predicted that U_CONS has a significant positive effect on the COEC however, C_CONS has a negative impact. Findings suggest also that the effect of the two dimensions of accounting conservatism differs considerably across COEC quantiles. Comparing results from QR method with those of OLS, this study throws more lights on the association between accounting conservatism and COEC. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=unconditional%20conservatism" title="unconditional conservatism">unconditional conservatism</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20conservatism" title=" conditional conservatism"> conditional conservatism</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20of%20equity%20capital" title=" cost of equity capital"> cost of equity capital</a>, <a href="https://publications.waset.org/abstracts/search?q=OLS" title=" OLS"> OLS</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression" title=" quantile regression"> quantile regression</a>, <a href="https://publications.waset.org/abstracts/search?q=emerging%20markets" title=" emerging markets"> emerging markets</a>, <a href="https://publications.waset.org/abstracts/search?q=MENA%20countries" title=" MENA countries"> MENA countries</a> </p> <a href="https://publications.waset.org/abstracts/18731/the-impact-of-unconditional-and-conditional-conservatism-on-cost-of-equity-capital-a-quantile-regression-approach-for-mena-countries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18731.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">359</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3223</span> Islamic Equity Markets Response to Volatility of Bitcoin</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zakaria%20S.%20G.%20Hegazy">Zakaria S. G. Hegazy</a>, <a href="https://publications.waset.org/abstracts/search?q=Walid%20M.%20A.%20Ahmed"> Walid M. A. Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper examines the dependence structure of Islamic stock markets on Bitcoin’s realized volatility components in bear, normal, and bull market periods. A quantile regression approach is employed, after adjusting raw returns with respect to a broad set of relevant global factors and accounting for structural breaks in the data. The results reveal that upside volatility tends to exert negative influences on Islamic developed-market returns more in bear than in bull market conditions, while downside volatility positively affects returns during bear and bull conditions. For emerging markets, we find that the upside (downside) component exerts lagged negative (positive) effects on returns in bear (all) market regimes. By and large, the dependence structures turn out to be asymmetric. Our evidence provides essential implications for investors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cryptocurrency%20markets" title="cryptocurrency markets">cryptocurrency markets</a>, <a href="https://publications.waset.org/abstracts/search?q=bitcoin" title=" bitcoin"> bitcoin</a>, <a href="https://publications.waset.org/abstracts/search?q=realized%20volatility%20measures" title=" realized volatility measures"> realized volatility measures</a>, <a href="https://publications.waset.org/abstracts/search?q=asymmetry" title=" asymmetry"> asymmetry</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression" title=" quantile regression"> quantile regression</a> </p> <a href="https://publications.waset.org/abstracts/141351/islamic-equity-markets-response-to-volatility-of-bitcoin" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141351.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">187</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3222</span> Estimation and Forecasting with a Quantile AR Model for Financial Returns </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuzhi%20Cai">Yuzhi Cai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This talk presents a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. We establish that the joint posterior distribution of the model parameters and future values is well defined. The associated MCMC algorithm for parameter estimation and forecasting converges to the posterior distribution quickly. We also present a combining forecasts technique to produce more accurate out-of-sample forecasts by using a weighted sequence of fitted QAR models. A moving window method to check the quality of the estimated conditional quantiles is developed. We verify our methodology using simulation studies and then apply it to currency exchange rate data. An application of the method to the USD to GBP daily currency exchange rates will also be discussed. The results obtained show that an unequally weighted combining method performs better than other forecasting methodology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=combining%20forecasts" title="combining forecasts">combining forecasts</a>, <a href="https://publications.waset.org/abstracts/search?q=MCMC" title=" MCMC"> MCMC</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20modelling" title=" quantile modelling"> quantile modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20forecasting" title=" quantile forecasting"> quantile forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20density%20functions" title=" predictive density functions"> predictive density functions</a> </p> <a href="https://publications.waset.org/abstracts/33437/estimation-and-forecasting-with-a-quantile-ar-model-for-financial-returns" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33437.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">347</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3221</span> An Application of Quantile Regression to Large-Scale Disaster Research</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Katarzyna%20Wyka">Katarzyna Wyka</a>, <a href="https://publications.waset.org/abstracts/search?q=Dana%20Sylvan"> Dana Sylvan</a>, <a href="https://publications.waset.org/abstracts/search?q=JoAnn%20Difede"> JoAnn Difede</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background and significance: The following disaster, population-based screening programs are routinely established to assess physical and psychological consequences of exposure. These data sets are highly skewed as only a small percentage of trauma-exposed individuals develop health issues. Commonly used statistical methodology in post-disaster mental health generally involves population-averaged models. Such models aim to capture the overall response to the disaster and its aftermath; however, they may not be sensitive enough to accommodate population heterogeneity in symptomatology, such as post-traumatic stress or depressive symptoms. Methods: We use an archival longitudinal data set from Weill-Cornell 9/11 Mental Health Screening Program established following the World Trade Center (WTC) terrorist attacks in New York in 2001. Participants are rescue and recovery workers who participated in the site cleanup and restoration (n=2960). The main outcome is the post-traumatic stress symptoms (PTSD) severity score assessed via clinician interviews (CAPS). For a detailed understanding of response to the disaster and its aftermath, we are adapting quantile regression methodology with particular focus on predictors of extreme distress and resilience to trauma. Results: The response variable was defined as the quantile of the CAPS score for each individual under two different scenarios specifying the unconditional quantiles based on: 1) clinically meaningful CAPS cutoff values and 2) CAPS distribution in the population. We present graphical summaries of the differential effects. For instance, we found that the effect of the WTC exposures, namely seeing bodies and feeling that life was in danger during rescue/recovery work was associated with very high PTSD symptoms. A similar effect was apparent in individuals with prior psychiatric history. Differential effects were also present for age and education level of the individuals. Conclusion: We evaluate the utility of quantile regression in disaster research in contrast to the commonly used population-averaged models. We focused on assessing the distribution of risk factors for post-traumatic stress symptoms across quantiles. This innovative approach provides a comprehensive understanding of the relationship between dependent and independent variables and could be used for developing tailored training programs and response plans for different vulnerability groups. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=disaster%20workers" title="disaster workers">disaster workers</a>, <a href="https://publications.waset.org/abstracts/search?q=post%20traumatic%20stress" title=" post traumatic stress"> post traumatic stress</a>, <a href="https://publications.waset.org/abstracts/search?q=PTSD" title=" PTSD"> PTSD</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression" title=" quantile regression"> quantile regression</a> </p> <a href="https://publications.waset.org/abstracts/60962/an-application-of-quantile-regression-to-large-scale-disaster-research" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60962.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">284</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3220</span> Determinants of Free Independent Traveler Tourist Expenditures in Israel: Quantile Regression Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shlomit%20Hon-Snir">Shlomit Hon-Snir</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharon%20Teitler-Regev"> Sharon Teitler-Regev</a>, <a href="https://publications.waset.org/abstracts/search?q=Anabel%20Lifszyc%20Friedlander"> Anabel Lifszyc Friedlander</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tourism, one of the world's largest and fastest growing industries, exerts a major economic influence. The number of international tourists is growing every year, and the relative portion of independent (FIT) tourists is growing as well. The characteristics of independent tourists differ from those of tourists who travel in organized trips. The purpose of the research is to identify the factors that affect the individual tourist's expenses in Israel: total expenses, expenses per day, expenses per tourist, expenses per day per tourist, accommodation expenses, dining expenses and transportation expenses. Most of the research analyzed the total expenses using OLS regression. The determinants influencing expenses were divided into four groups: budget constraints, socio-demographic data, psychological characteristics and travel-related characteristics. Since the effect of each variable may change over different levels of total expenses the quantile regression (QR) theory will be applied. The current research will use data collected by the Israeli Ministry of Tourism in 2015 from individual independent tourists at the end of their visit to Israel. Preliminary results show that: At lower levels of expense, only income has a (positive) effect on total expenses, while at higher levels of expense, both income and length of stay have (positive) effects. -The effect of income on total expenses is higher for higher levels of expenses than for lower level of expenses. -The number of sites visited during the trip has a (negative) effect on tourist accommodation expenses only for tourists with a high level of total expenses. Due to the increasing share of independent tourism in Israel and around the world and due to the importance of tourism to Israel, it is very important to understand the factors that influence the expenses and behavior of independent tourists. Understanding the factors that affect independent tourists' expenses in Israel can help Israeli policymakers in their promotional efforts to attract tourism to Israel. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=independent%20tourist" title="independent tourist">independent tourist</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression%20theory" title=" quantile regression theory"> quantile regression theory</a>, <a href="https://publications.waset.org/abstracts/search?q=tourism%20expenses" title=" tourism expenses"> tourism expenses</a>, <a href="https://publications.waset.org/abstracts/search?q=tourism" title=" tourism"> tourism</a> </p> <a href="https://publications.waset.org/abstracts/72458/determinants-of-free-independent-traveler-tourist-expenditures-in-israel-quantile-regression-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72458.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">274</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3219</span> Corporate Sustainability Practices in Asian Countries: Pattern of Disclosure and Impact on Financial Performance </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Santi%20Gopal%20Maji">Santi Gopal Maji</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20A.%20J.%20Syngkon"> R. A. J. Syngkon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The changing attitude of the corporate enterprises from maximizing economic benefit to corporate sustainability after the publication of Brundtland Report has attracted the interest of researchers to investigate the sustainability practices of firms and its impact on financial performance. To enrich the empirical literature in Asian context, this study examines the disclosure pattern of corporate sustainability and the influence of sustainability reporting on financial performance of firms from four Asian countries (Japan, South Korea, India and Indonesia) that are publishing sustainability report continuously from 2009 to 2016. The study has used content analysis technique based on Global Reporting Framework (3 and 3.1) reporting framework to compute the disclosure score of corporate sustainability and its components. While dichotomous coding system has been employed to compute overall quantitative disclosure score, a four-point scale has been used to access the quality of the disclosure. For analysing the disclosure pattern of corporate sustainability, box plot has been used. Further, Pearson chi-square test has been used to examine whether there is any difference in the proportion of disclosure between the countries. Finally, quantile regression model has been employed to examine the influence of corporate sustainability reporting on the difference locations of the conditional distribution of firm performance. The findings of the study indicate that Japan has occupied first position in terms of disclosure of sustainability information followed by South Korea and India. In case of Indonesia, the quality of disclosure score is considerably less as compared to other three countries. Further, the gap between the quality and quantity of disclosure score is comparatively less in Japan and South Korea as compared to India and Indonesia. The same is evident in respect of the components of sustainability. The results of quantile regression indicate that a positive impact of corporate sustainability becomes stronger at upper quantiles in case of Japan and South Korea. But the study fails to extricate any definite pattern on the impact of corporate sustainability disclosure on the financial performance of firms from Indonesia and India. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corporate%20sustainability" title="corporate sustainability">corporate sustainability</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20and%20quantity%20of%20disclosure" title=" quality and quantity of disclosure"> quality and quantity of disclosure</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20analysis" title=" content analysis"> content analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression" title=" quantile regression"> quantile regression</a>, <a href="https://publications.waset.org/abstracts/search?q=Asian%20countries" title=" Asian countries"> Asian countries</a> </p> <a href="https://publications.waset.org/abstracts/97073/corporate-sustainability-practices-in-asian-countries-pattern-of-disclosure-and-impact-on-financial-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97073.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">194</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3218</span> Generalized Extreme Value Regression with Binary Dependent Variable: An Application for Predicting Meteorological Drought Probabilities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Retius%20Chifurira">Retius Chifurira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Logistic regression model is the most used regression model to predict meteorological drought probabilities. When the dependent variable is extreme, the logistic model fails to adequately capture drought probabilities. In order to adequately predict drought probabilities, we use the generalized linear model (GLM) with the quantile function of the generalized extreme value distribution (GEVD) as the link function. The method maximum likelihood estimation is used to estimate the parameters of the generalized extreme value (GEV) regression model. We compare the performance of the logistic and the GEV regression models in predicting drought probabilities for Zimbabwe. The performance of the regression models are assessed using the goodness-of-fit tests, namely; relative root mean square error (RRMSE) and relative mean absolute error (RMAE). Results show that the GEV regression model performs better than the logistic model, thereby providing a good alternative candidate for predicting drought probabilities. This paper provides the first application of GLM derived from extreme value theory to predict drought probabilities for a drought-prone country such as Zimbabwe. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20extreme%20value%20distribution" title="generalized extreme value distribution">generalized extreme value distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=general%20linear%20model" title=" general linear model"> general linear model</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20annual%20rainfall" title=" mean annual rainfall"> mean annual rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=meteorological%20drought%20probabilities" title=" meteorological drought probabilities"> meteorological drought probabilities</a> </p> <a href="https://publications.waset.org/abstracts/99321/generalized-extreme-value-regression-with-binary-dependent-variable-an-application-for-predicting-meteorological-drought-probabilities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99321.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">200</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3217</span> Non-Parametric, Unconditional Quantile Estimation of Efficiency in Microfinance Institutions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Komlan%20Sedzro">Komlan Sedzro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We apply the non-parametric, unconditional, hyperbolic order-α quantile estimator to appraise the relative efficiency of Microfinance Institutions in Africa in terms of outreach. Our purpose is to verify if these institutions, which must constantly try to strike a compromise between their social role and financial sustainability are operationally efficient. Using data on African MFIs extracted from the Microfinance Information eXchange (MIX) database and covering the 2004 to 2006 periods, we find that more efficient MFIs are also the most profitable. This result is in line with the view that social performance is not in contradiction with the pursuit of excellent financial performance. Our results also show that large MFIs in terms of asset and those charging the highest fees are not necessarily the most efficient. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20envelopment%20analysis" title="data envelopment analysis">data envelopment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=microfinance%20institutions" title=" microfinance institutions"> microfinance institutions</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20estimation%20of%20efficiency" title=" quantile estimation of efficiency"> quantile estimation of efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20and%20financial%20performance" title=" social and financial performance"> social and financial performance</a> </p> <a href="https://publications.waset.org/abstracts/31841/non-parametric-unconditional-quantile-estimation-of-efficiency-in-microfinance-institutions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31841.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">308</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3216</span> Poverty Dynamics in Thailand: Evidence from Household Panel Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nattabhorn%20Leamcharaskul">Nattabhorn Leamcharaskul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to examine determining factors of the dynamics of poverty in Thailand by using panel data of 3,567 households in 2007-2017. Four techniques of estimation are employed to analyze the situation of poverty across households and time periods: the multinomial logit model, the sequential logit model, the quantile regression model, and the difference in difference model. Households are categorized based on their experiences into 5 groups, namely chronically poor, falling into poverty, re-entering into poverty, exiting from poverty and never poor households. Estimation results emphasize the effects of demographic and socioeconomic factors as well as unexpected events on the economic status of a household. It is found that remittances have positive impact on household’s economic status in that they are likely to lower the probability of falling into poverty or trapping in poverty while they tend to increase the probability of exiting from poverty. In addition, not only receiving a secondary source of household income can raise the probability of being a never poor household, but it also significantly increases household income per capita of the chronically poor and falling into poverty households. Public work programs are recommended as an important tool to relieve household financial burden and uncertainty and thus consequently increase a chance for households to escape from poverty. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=difference%20in%20difference" title="difference in difference">difference in difference</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic" title=" dynamic"> dynamic</a>, <a href="https://publications.waset.org/abstracts/search?q=multinomial%20logit%20model" title=" multinomial logit model"> multinomial logit model</a>, <a href="https://publications.waset.org/abstracts/search?q=panel%20data" title=" panel data"> panel data</a>, <a href="https://publications.waset.org/abstracts/search?q=poverty" title=" poverty"> poverty</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression" title=" quantile regression"> quantile regression</a>, <a href="https://publications.waset.org/abstracts/search?q=remittance" title=" remittance"> remittance</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20logit%20model" title=" sequential logit model"> sequential logit model</a>, <a href="https://publications.waset.org/abstracts/search?q=Thailand" title=" Thailand"> Thailand</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer" title=" transfer"> transfer</a> </p> <a href="https://publications.waset.org/abstracts/165351/poverty-dynamics-in-thailand-evidence-from-household-panel-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165351.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">112</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3215</span> Forecasting for Financial Stock Returns Using a Quantile Function Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuzhi%20Cai">Yuzhi Cai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we introduce a newly developed quantile function model that can be used for estimating conditional distributions of financial returns and for obtaining multi-step ahead out-of-sample predictive distributions of financial returns. Since we forecast the whole conditional distributions, any predictive quantity of interest about the future financial returns can be obtained simply as a by-product of the method. We also show an application of the model to the daily closing prices of Dow Jones Industrial Average (DJIA) series over the period from 2 January 2004 - 8 October 2010. We obtained the predictive distributions up to 15 days ahead for the DJIA returns, which were further compared with the actually observed returns and those predicted from an AR-GARCH model. The results show that the new model can capture the main features of financial returns and provide a better fitted model together with improved mean forecasts compared with conventional methods. We hope this talk will help audience to see that this new model has the potential to be very useful in practice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DJIA" title="DJIA">DJIA</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20returns" title=" financial returns"> financial returns</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20distribution" title=" predictive distribution"> predictive distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20function%20model" title=" quantile function model"> quantile function model</a> </p> <a href="https://publications.waset.org/abstracts/33434/forecasting-for-financial-stock-returns-using-a-quantile-function-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33434.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">367</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3214</span> The Effectiveness of Energy-related Tax in Curbing Transport-related Carbon Emissions: The Role of Green Finance and Technology in OECD Economies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Taimoor">Hassan Taimoor</a>, <a href="https://publications.waset.org/abstracts/search?q=Piotr%20Krajewski"> Piotr Krajewski</a>, <a href="https://publications.waset.org/abstracts/search?q=Piotr%20Gabrielzcak"> Piotr Gabrielzcak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Being responsible for the largest source of energy-related emissions, the transportation sector is driven by more than half of global oil demand and total energy consumption, making it a crucial factor in tackling climate change and environmental degradation. The present study empirically tests the effectives of the energy-related tax (TXEN) in curbing transport-related carbon emissions (CO2TRANSP) in Organization for Economic Cooperation and Development (OECD) economies over the period of 1990-2020. Moreover, Green Finance (GF), Technology (TECH), and Gross domestic product (GDP) have also been added as explanatory factors which might affect CO2TRANSP emissions. The study employs the Method of Moment Quantile Regression (MMQR), an advance econometric technique to observe the variations along each quantile. Based on the results of the preliminary test, we confirm the presence of cross-sectional dependence and slope heterogeneity. Whereas the result of the panel unit root test report mixed order of variables’ integration. The findings reveal that rise in income level activates CO2TRANSP, confirming the first stage of Environmental Kuznet Hypothesis. Surprisingly, the present TXEN policies of OECD member states are not mature enough to tackle the CO2TRANSP emissions. However, the findings confirm that GF and TECH are solely responsible for the reduction in the CO2TRANSP. The outcomes of Bootstrap Quantile Regression (BSQR) further validate and support the earlier findings of MMQR. Based on the findings of this study, it is revealed that the current TXEN policies are too moderate, and an incremental and progressive rise in TXEN may help in a transition toward a cleaner and sustainable transportation sector in the study region. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transport-related%20CO2%20emissions" title="transport-related CO2 emissions">transport-related CO2 emissions</a>, <a href="https://publications.waset.org/abstracts/search?q=energy-related%20tax" title=" energy-related tax"> energy-related tax</a>, <a href="https://publications.waset.org/abstracts/search?q=green%20finance" title=" green finance"> green finance</a>, <a href="https://publications.waset.org/abstracts/search?q=technological%20development" title=" technological development"> technological development</a>, <a href="https://publications.waset.org/abstracts/search?q=oecd%20member%20states" title=" oecd member states"> oecd member states</a> </p> <a href="https://publications.waset.org/abstracts/164396/the-effectiveness-of-energy-related-tax-in-curbing-transport-related-carbon-emissions-the-role-of-green-finance-and-technology-in-oecd-economies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164396.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">77</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3213</span> Investigating The Nexus Between Energy Deficiency, Environmental Sustainability and Renewable Energy: The Role of Energy Trade in Global Perspectives</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fahim%20Ullah">Fahim Ullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Usman"> Muhammad Usman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Energy consumption and environmental sustainability are hard challenges of 21st century. Energy richness increases environmental pollution while energy poverty hinders economic growth. Considering these two aspects, present study calculates energy deficiency and examines the role of renewable energy to overcome rising energy deficiency and carbon emission for selected countries from 1990 to 2021. For empirical analysis, this study uses methods of moments panel quantile regression analysis and to check the robustness, study used panel quantile robust analysis. Graphical analysis indicated rising global energy deficiency since last three decades where energy consumption is higher than energy production. Empirical results showed that renewable energy is a significant factor for reducing energy deficiency. Secondly, the energy deficiency increases carbon emission level and again renewable energy decreases emissions level. This study recommends that global energy deficiency and rising carbon emissions can be controlled through structural change in the form of energy transition to replace non-renewable resources with renewable resources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20deficiency" title="energy deficiency">energy deficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=renewable%20energy" title=" renewable energy"> renewable energy</a>, <a href="https://publications.waset.org/abstracts/search?q=carbon%20emission" title=" carbon emission"> carbon emission</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20trade" title=" energy trade"> energy trade</a>, <a href="https://publications.waset.org/abstracts/search?q=PQL%20analysis" title=" PQL analysis"> PQL analysis</a> </p> <a href="https://publications.waset.org/abstracts/183640/investigating-the-nexus-between-energy-deficiency-environmental-sustainability-and-renewable-energy-the-role-of-energy-trade-in-global-perspectives" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183640.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">64</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3212</span> Accelerating Sustainable Urban Transition Through Green Technology Innovation and Clean Energy to Achieve Net Zero Emissions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emma%20Serwaa%20Obobisa">Emma Serwaa Obobisa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Urbanization has become the focus for challenging goals relating to environmental performance, such as carbon neutrality. Green technological innovation and clean energy are considered the prominent factors in reducing emissions and achieving sustainable cities. Through the application of a fixed effect model, generalized method of moments, and quantile-on-quantile regression, this study explores the role of green technology innovation and clean energy in accelerating the sustainable urban transition towards net zero emissions in developing countries while controlling for nonrenewable energy consumption, and economic growth. The long-run results show that green technology innovation and renewable energy consumption reduce CO₂ emissions from urban residential buildings. In contrast, economic growth and nonrenewable energy consumption increase CO₂ emissions. This study proposes a consistent technique for encouraging green technological innovation and renewable energy projects in developing countries where the role of innovation in achieving carbon neutrality is still understudied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=green%20technology%20innovation" title="green technology innovation">green technology innovation</a>, <a href="https://publications.waset.org/abstracts/search?q=renewable%20energy" title=" renewable energy"> renewable energy</a>, <a href="https://publications.waset.org/abstracts/search?q=urbanization" title=" urbanization"> urbanization</a>, <a href="https://publications.waset.org/abstracts/search?q=net%20zero%20emissions" title=" net zero emissions"> net zero emissions</a> </p> <a href="https://publications.waset.org/abstracts/186132/accelerating-sustainable-urban-transition-through-green-technology-innovation-and-clean-energy-to-achieve-net-zero-emissions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186132.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">34</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3211</span> Reliability Based Investigation on the Choice of Characteristic Soil Properties</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jann-Eike%20Saathoff">Jann-Eike Saathoff</a>, <a href="https://publications.waset.org/abstracts/search?q=Kirill%20Alexander%20Schmoor"> Kirill Alexander Schmoor</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Achmus"> Martin Achmus</a>, <a href="https://publications.waset.org/abstracts/search?q=Mauricio%20Terceros"> Mauricio Terceros</a> </p> <p class="card-text"><strong>Abstract:</strong></p> By using partial factors of safety, uncertainties due to the inherent variability of the soil properties and loads are taken into account in the geotechnical design process. According to the reliability index concept in Eurocode-0 in conjunction with Eurocode-7 a minimum safety level of β = 3.8 for reliability class RC2 shall be established. The reliability of the system depends heavily on the choice of the prespecified safety factor and the choice of the characteristic soil properties. The safety factors stated in the standards are mainly based on experience. However, no general accepted method for the calculation of a characteristic value within the current design practice exists. In this study, a laterally loaded monopile is investigated and the influence of the chosen quantile values of the deterministic system, calculated with p-y springs, will be presented. Monopiles are the most common foundation concepts for offshore wind energy converters. Based on the calculations for non-cohesive soils, a recommendation for an appropriate quantile value for the necessary safety level according to the standards for a deterministic design is given. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asymptotic%20sampling" title="asymptotic sampling">asymptotic sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=characteristic%20value" title=" characteristic value"> characteristic value</a>, <a href="https://publications.waset.org/abstracts/search?q=monopile%20foundation" title=" monopile foundation"> monopile foundation</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20design" title=" probabilistic design"> probabilistic design</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20values" title=" quantile values"> quantile values</a> </p> <a href="https://publications.waset.org/abstracts/101485/reliability-based-investigation-on-the-choice-of-characteristic-soil-properties" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101485.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3210</span> Behind Fuzzy Regression Approach: An Exploration Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lavinia%20B.%20Dulla">Lavinia B. Dulla</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The exploration study of the fuzzy regression approach attempts to present that fuzzy regression can be used as a possible alternative to classical regression. It likewise seeks to assess the differences and characteristics of simple linear regression and fuzzy regression using the width of prediction interval, mean absolute deviation, and variance of residuals. Based on the simple linear regression model, the fuzzy regression approach is worth considering as an alternative to simple linear regression when the sample size is between 10 and 20. As the sample size increases, the fuzzy regression approach is not applicable to use since the assumption regarding large sample size is already operating within the framework of simple linear regression. Nonetheless, it can be suggested for a practical alternative when decisions often have to be made on the basis of small data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20regression%20approach" title="fuzzy regression approach">fuzzy regression approach</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20fuzziness%20criterion" title=" minimum fuzziness criterion"> minimum fuzziness criterion</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20regression" title=" interval regression"> interval regression</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20interval" title=" prediction interval"> prediction interval</a> </p> <a href="https://publications.waset.org/abstracts/139364/behind-fuzzy-regression-approach-an-exploration-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139364.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">298</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=quantile%20regression&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=quantile%20regression&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=quantile%20regression&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=quantile%20regression&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=quantile%20regression&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=quantile%20regression&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=quantile%20regression&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=quantile%20regression&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=quantile%20regression&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=quantile%20regression&page=107">107</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=quantile%20regression&page=108">108</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=quantile%20regression&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>