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likelihood uncertainty estimation</title> <meta name="description" content="Search results for: generalized likelihood uncertainty estimation"> <meta name="keywords" content="generalized likelihood uncertainty estimation"> <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" 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style="font-size:1.6rem;">Search results for: generalized likelihood uncertainty estimation</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3985</span> The Generalized Pareto Distribution as a Model for Sequential Order Statistics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdy%20%E2%80%8EEsmailian">Mahdy ‎Esmailian</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20%E2%80%8EDoostparast"> Mahdi ‎Doostparast</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20%E2%80%8EParsian"> Ahmad ‎Parsian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> ‎In this article‎, ‎sequential order statistics (SOS) censoring type II samples coming from the generalized Pareto distribution are considered‎. ‎Maximum likelihood (ML) estimators of the unknown parameters are derived on the basis of the available multiple SOS data‎. ‎Necessary conditions for existence and uniqueness of the derived ML estimates are given‎. Due to complexity in the proposed likelihood function‎, ‎a useful re-parametrization is suggested‎. ‎For illustrative purposes‎, ‎a Monte Carlo simulation study is conducted and an illustrative example is analysed‎. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayesian%20estimation%E2%80%8E" title="bayesian estimation‎">bayesian estimation‎</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20pareto%20distribution%E2%80%8E" title=" generalized pareto distribution‎"> generalized pareto distribution‎</a>, <a href="https://publications.waset.org/abstracts/search?q=%E2%80%8Emaximum%20likelihood%20%20estimation%E2%80%8E" title=" ‎maximum likelihood estimation‎"> ‎maximum likelihood estimation‎</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20order%20statistics" title=" sequential order statistics"> sequential order statistics</a> </p> <a href="https://publications.waset.org/abstracts/26988/the-generalized-pareto-distribution-as-a-model-for-sequential-order-statistics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26988.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">509</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">3984</span> Point Estimation for the Type II Generalized Logistic Distribution Based on Progressively Censored Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rana%20Rimawi">Rana Rimawi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayman%20Baklizi"> Ayman Baklizi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Skewed distributions are important models that are frequently used in applications. Generalized distributions form a class of skewed distributions and gain widespread use in applications because of their flexibility in data analysis. More specifically, the Generalized Logistic Distribution with its different types has received considerable attention recently. In this study, based on progressively type-II censored data, we will consider point estimation in type II Generalized Logistic Distribution (Type II GLD). We will develop several estimators for its unknown parameters, including maximum likelihood estimators (MLE), Bayes estimators and linear estimators (BLUE). The estimators will be compared using simulation based on the criteria of bias and Mean square error (MSE). An illustrative example of a real data set will be given. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=point%20estimation" title="point estimation">point estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=type%20II%20generalized%20logistic%20distribution" title=" type II generalized logistic distribution"> type II generalized logistic distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=progressive%20censoring" title=" progressive censoring"> progressive censoring</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a> </p> <a href="https://publications.waset.org/abstracts/142979/point-estimation-for-the-type-ii-generalized-logistic-distribution-based-on-progressively-censored-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142979.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">198</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">3983</span> Parameter Estimation for the Mixture of Generalized Gamma Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wikanda%20Phaphan">Wikanda Phaphan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mixture generalized gamma distribution is a combination of two distributions: generalized gamma distribution and length biased generalized gamma distribution. These two distributions were presented by Suksaengrakcharoen and Bodhisuwan in 2014. The findings showed that probability density function (pdf) had fairly complexities, so it made problems in estimating parameters. The problem occurred in parameter estimation was that we were unable to calculate estimators in the form of critical expression. Thus, we will use numerical estimation to find the estimators. In this study, we presented a new method of the parameter estimation by using the expectation – maximization algorithm (EM), the conjugate gradient method, and the quasi-Newton method. The data was generated by acceptance-rejection method which is used for estimating α, β, λ and p. λ is the scale parameter, p is the weight parameter, α and β are the shape parameters. We will use Monte Carlo technique to find the estimator's performance. Determining the size of sample equals 10, 30, 100; the simulations were repeated 20 times in each case. We evaluated the effectiveness of the estimators which was introduced by considering values of the mean squared errors and the bias. The findings revealed that the EM-algorithm had proximity to the actual values determined. Also, the maximum likelihood estimators via the conjugate gradient and the quasi-Newton method are less precision than the maximum likelihood estimators via the EM-algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conjugate%20gradient%20method" title="conjugate gradient method">conjugate gradient method</a>, <a href="https://publications.waset.org/abstracts/search?q=quasi-Newton%20method" title=" quasi-Newton method"> quasi-Newton method</a>, <a href="https://publications.waset.org/abstracts/search?q=EM-algorithm" title=" EM-algorithm"> EM-algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20gamma%20distribution" title=" generalized gamma distribution"> generalized gamma distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=length%20biased%20generalized%20gamma%20distribution" title=" length biased generalized gamma distribution"> length biased generalized gamma distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20method" title=" maximum likelihood method"> maximum likelihood method</a> </p> <a href="https://publications.waset.org/abstracts/81404/parameter-estimation-for-the-mixture-of-generalized-gamma-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81404.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">219</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">3982</span> Frequency Analysis of Minimum Ecological Flow and Gage Height in Indus River Using Maximum Likelihood Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tasir%20Khan">Tasir Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Yejuan%20Wan"> Yejuan Wan</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalim%20Ullah"> Kalim Ullah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hydrological frequency analysis has been conducted to estimate the minimum flow elevation of the Indus River in Pakistan to protect the ecosystem. The Maximum likelihood estimation (MLE) technique is used to estimate the best-fitted distribution for Minimum Ecological Flows at nine stations of the Indus River in Pakistan. The four selected distributions, Generalized Extreme Value (GEV) distribution, Generalized Logistics (GLO) distribution, Generalized Pareto (GPA) distribution, and Pearson type 3 (PE3) are fitted in all sites, usually used in hydro frequency analysis. Compare the performance of these distributions by using the goodness of fit tests, such as the Kolmogorov Smirnov test, Anderson darling test, and chi-square test. The study concludes that the Maximum Likelihood Estimation (MLE) method recommended that GEV and GPA are the most suitable distributions which can be effectively applied to all the proposed sites. The quantiles are estimated for the return periods from 5 to 1000 years by using MLE, estimations methods. The MLE is the robust method for larger sample sizes. The results of these analyses can be used for water resources research, including water quality management, designing irrigation systems, determining downstream flow requirements for hydropower, and the impact of long-term drought on the country's aquatic system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=minimum%20ecological%20flow" title="minimum ecological flow">minimum ecological flow</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20distribution" title=" frequency distribution"> frequency distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=indus%20river" title=" indus river"> indus river</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a> </p> <a href="https://publications.waset.org/abstracts/161795/frequency-analysis-of-minimum-ecological-flow-and-gage-height-in-indus-river-using-maximum-likelihood-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161795.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">3981</span> A Survey on Quasi-Likelihood Estimation Approaches for Longitudinal Set-ups</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naushad%20Mamode%20Khan">Naushad Mamode Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Com-Poisson (CMP) model is one of the most popular discrete generalized linear models (GLMS) that handles both equi-, over- and under-dispersed data. In longitudinal context, an integer-valued autoregressive (INAR(1)) process that incorporates covariate specification has been developed to model longitudinal CMP counts. However, the joint likelihood CMP function is difficult to specify and thus restricts the likelihood based estimating methodology. The joint generalized quasilikelihood approach (GQL-I) was instead considered but is rather computationally intensive and may not even estimate the regression effects due to a complex and frequently ill conditioned covariance structure. This paper proposes a new GQL approach for estimating the regression parameters (GQLIII) that are based on a single score vector representation. The performance of GQL-III is compared with GQL-I and separate marginal GQLs (GQL-II) through some simulation experiments and is proved to yield equally efficient estimates as GQL-I and is far more computationally stable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=longitudinal" title="longitudinal">longitudinal</a>, <a href="https://publications.waset.org/abstracts/search?q=com-Poisson" title=" com-Poisson"> com-Poisson</a>, <a href="https://publications.waset.org/abstracts/search?q=ill-conditioned" title=" ill-conditioned"> ill-conditioned</a>, <a href="https://publications.waset.org/abstracts/search?q=INAR%281%29" title=" INAR(1)"> INAR(1)</a>, <a href="https://publications.waset.org/abstracts/search?q=GLMS" title=" GLMS"> GLMS</a>, <a href="https://publications.waset.org/abstracts/search?q=GQL" title=" GQL"> GQL</a> </p> <a href="https://publications.waset.org/abstracts/40051/a-survey-on-quasi-likelihood-estimation-approaches-for-longitudinal-set-ups" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40051.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">354</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">3980</span> An Application of Sinc Function to Approximate Quadrature Integrals in Generalized Linear Mixed Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Altaf%20H.%20Khan">Altaf H. Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Frank%20Stenger"> Frank Stenger</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20A.%20Hussein"> Mohammed A. Hussein</a>, <a href="https://publications.waset.org/abstracts/search?q=Reaz%20A.%20Chaudhuri"> Reaz A. Chaudhuri</a>, <a href="https://publications.waset.org/abstracts/search?q=Sameera%20Asif"> Sameera Asif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses a novel approach to approximate quadrature integrals that arise in the estimation of likelihood parameters for the generalized linear mixed models (GLMM) as well as Bayesian methodology also requires computation of multidimensional integrals with respect to the posterior distributions in which computation are not only tedious and cumbersome rather in some situations impossible to find solutions because of singularities, irregular domains, etc. An attempt has been made in this work to apply Sinc function based quadrature rules to approximate intractable integrals, as there are several advantages of using Sinc based methods, for example: order of convergence is exponential, works very well in the neighborhood of singularities, in general quite stable and provide high accurate and double precisions estimates. The Sinc function based approach seems to be utilized first time in statistical domain to our knowledge, and it's viability and future scopes have been discussed to apply in the estimation of parameters for GLMM models as well as some other statistical areas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20linear%20mixed%20model" title="generalized linear mixed model">generalized linear mixed model</a>, <a href="https://publications.waset.org/abstracts/search?q=likelihood%20parameters" title=" likelihood parameters"> likelihood parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=qudarature" title=" qudarature"> qudarature</a>, <a href="https://publications.waset.org/abstracts/search?q=Sinc%20function" title=" Sinc function"> Sinc function</a> </p> <a href="https://publications.waset.org/abstracts/39637/an-application-of-sinc-function-to-approximate-quadrature-integrals-in-generalized-linear-mixed-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39637.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">395</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">3979</span> Tracing Sources of Sediment in an Arid River, Southern Iran</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hesam%20Gholami">Hesam Gholami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Elevated suspended sediment loads in riverine systems resulting from accelerated erosion due to human activities are a serious threat to the sustainable management of watersheds and ecosystem services therein worldwide. Therefore, mitigation of deleterious sediment effects as a distributed or non-point pollution source in the catchments requires reliable provenance information. Sediment tracing or sediment fingerprinting, as a combined process consisting of sampling, laboratory measurements, different statistical tests, and the application of mixing or unmixing models, is a useful technique for discriminating the sources of sediments. From 1996 to the present, different aspects of this technique, such as grouping the sources (spatial and individual sources), discriminating the potential sources by different statistical techniques, and modification of mixing and unmixing models, have been introduced and modified by many researchers worldwide, and have been applied to identify the provenance of fine materials in agricultural, rural, mountainous, and coastal catchments, and in large catchments with numerous lakes and reservoirs. In the last two decades, efforts exploring the uncertainties associated with sediment fingerprinting results have attracted increasing attention. The frameworks used to quantify the uncertainty associated with fingerprinting estimates can be divided into three groups comprising Monte Carlo simulation, Bayesian approaches and generalized likelihood uncertainty estimation (GLUE). Given the above background, the primary goal of this study was to apply geochemical fingerprinting within the GLUE framework in the estimation of sub-basin spatial sediment source contributions in the arid Mehran River catchment in southern Iran, which drains into the Persian Gulf. The accuracy of GLUE predictions generated using four different sets of statistical tests for discriminating three sub-basin spatial sources was evaluated using 10 virtual sediments (VS) samples with known source contributions using the root mean square error (RMSE) and mean absolute error (MAE). Based on the results, the contributions modeled by GLUE for the western, central and eastern sub-basins are 1-42% (overall mean 20%), 0.5-30% (overall mean 12%) and 55-84% (overall mean 68%), respectively. According to the mean absolute fit (MAF; ≥ 95% for all target sediment samples) and goodness-of-fit (GOF; ≥ 99% for all samples), our suggested modeling approach is an accurate technique to quantify the source of sediments in the catchments. Overall, the estimated source proportions can help watershed engineers plan the targeting of conservation programs for soil and water resources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sediment%20source%20tracing" title="sediment source tracing">sediment source tracing</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20likelihood%20uncertainty%20estimation" title=" generalized likelihood uncertainty estimation"> generalized likelihood uncertainty estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20sediment%20mixtures" title=" virtual sediment mixtures"> virtual sediment mixtures</a>, <a href="https://publications.waset.org/abstracts/search?q=Iran" title=" Iran"> Iran</a> </p> <a href="https://publications.waset.org/abstracts/166240/tracing-sources-of-sediment-in-an-arid-river-southern-iran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166240.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">74</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">3978</span> Modelling Hydrological Time Series Using Wakeby Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ilaria%20Lucrezia%20Amerise">Ilaria Lucrezia Amerise</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The statistical modelling of precipitation data for a given portion of territory is fundamental for the monitoring of climatic conditions and for Hydrogeological Management Plans (HMP). This modelling is rendered particularly complex by the changes taking place in the frequency and intensity of precipitation, presumably to be attributed to the global climate change. This paper applies the Wakeby distribution (with 5 parameters) as a theoretical reference model. The number and the quality of the parameters indicate that this distribution may be the appropriate choice for the interpolations of the hydrological variables and, moreover, the Wakeby is particularly suitable for describing phenomena producing heavy tails. The proposed estimation methods for determining the value of the Wakeby parameters are the same as those used for density functions with heavy tails. The commonly used procedure is the classic method of moments weighed with probabilities (probability weighted moments, PWM) although this has often shown difficulty of convergence, or rather, convergence to a configuration of inappropriate parameters. In this paper, we analyze the problem of the likelihood estimation of a random variable expressed through its quantile function. The method of maximum likelihood, in this case, is more demanding than in the situations of more usual estimation. The reasons for this lie, in the sampling and asymptotic properties of the estimators of maximum likelihood which improve the estimates obtained with indications of their variability and, therefore, their accuracy and reliability. These features are highly appreciated in contexts where poor decisions, attributable to an inefficient or incomplete information base, can cause serious damages. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20extreme%20values" title="generalized extreme values">generalized extreme values</a>, <a href="https://publications.waset.org/abstracts/search?q=likelihood%20estimation" title=" likelihood estimation"> likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=precipitation%20data" title=" precipitation data"> precipitation data</a>, <a href="https://publications.waset.org/abstracts/search?q=Wakeby%20distribution" title=" Wakeby distribution"> Wakeby distribution</a> </p> <a href="https://publications.waset.org/abstracts/105205/modelling-hydrological-time-series-using-wakeby-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105205.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">138</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">3977</span> Frequency Offset Estimation Schemes Based on ML for OFDM Systems in Non-Gaussian Noise Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Keunhong%20Chae">Keunhong Chae</a>, <a href="https://publications.waset.org/abstracts/search?q=Seokho%20Yoon"> Seokho Yoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, frequency offset (FO) estimation schemes robust to the non-Gaussian noise environments are proposed for orthogonal frequency division multiplexing (OFDM) systems. First, a maximum-likelihood (ML) estimation scheme in non-Gaussian noise environments is proposed, and then, the complexity of the ML estimation scheme is reduced by employing a reduced set of candidate values. In numerical results, it is demonstrated that the proposed schemes provide a significant performance improvement over the conventional estimation scheme in non-Gaussian noise environments while maintaining the performance similar to the estimation performance in Gaussian noise environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequency%20offset%20estimation" title="frequency offset estimation">frequency offset estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum-likelihood" title=" maximum-likelihood"> maximum-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=non-Gaussian%20noise%0D%0Aenvironment" title=" non-Gaussian noise environment"> non-Gaussian noise environment</a>, <a href="https://publications.waset.org/abstracts/search?q=OFDM" title=" OFDM"> OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=training%20symbol" title=" training symbol"> training symbol</a> </p> <a href="https://publications.waset.org/abstracts/9430/frequency-offset-estimation-schemes-based-on-ml-for-ofdm-systems-in-non-gaussian-noise-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9430.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">353</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">3976</span> ML-Based Blind Frequency Offset Estimation Schemes for OFDM Systems in Non-Gaussian Noise Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Keunhong%20Chae">Keunhong Chae</a>, <a href="https://publications.waset.org/abstracts/search?q=Seokho%20Yoon"> Seokho Yoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes frequency offset (FO) estimation schemes robust to the non-Gaussian noise for orthogonal frequency division multiplexing (OFDM) systems. A maximum-likelihood (ML) scheme and a low-complexity estimation scheme are proposed by applying the probability density function of the cyclic prefix of OFDM symbols to the ML criterion. From simulation results, it is confirmed that the proposed schemes offer a significant FO estimation performance improvement over the conventional estimation scheme in non-Gaussian noise environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequency%20offset" title="frequency offset">frequency offset</a>, <a href="https://publications.waset.org/abstracts/search?q=cyclic%20prefix" title=" cyclic prefix"> cyclic prefix</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum-likelihood" title=" maximum-likelihood"> maximum-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=non-Gaussian%0D%0Anoise" title=" non-Gaussian noise"> non-Gaussian noise</a>, <a href="https://publications.waset.org/abstracts/search?q=OFDM" title=" OFDM"> OFDM</a> </p> <a href="https://publications.waset.org/abstracts/10266/ml-based-blind-frequency-offset-estimation-schemes-for-ofdm-systems-in-non-gaussian-noise-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10266.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">476</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">3975</span> Residual Life Estimation Based on Multi-Phase Nonlinear Wiener Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hao%20Chen">Hao Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Guo"> Bo Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Ping%20Jiang"> Ping Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Residual life (RL) estimation based on multi-phase nonlinear Wiener process was studied in this paper, which is significant for complicated products with small samples. Firstly, nonlinear Wiener model with random parameter was introduced and multi-phase nonlinear Wiener model was proposed to model degradation process of products that were nonlinear and separated into different phases. Then the multi-phase RL probability density function based on the presented model was derived approximately in a closed form and parameters estimation was achieved with the method of maximum likelihood estimation (MLE). Finally, the method was applied to estimate the RL of high voltage plus capacitor. Compared with the other three different models by log-likelihood function (Log-LF) and Akaike information criterion (AIC), the results show that the proposed degradation model can capture degradation process of high voltage plus capacitors in a better way and provide a more reliable result. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-phase%20nonlinear%20wiener%20process" title="multi-phase nonlinear wiener process">multi-phase nonlinear wiener process</a>, <a href="https://publications.waset.org/abstracts/search?q=residual%20life%20estimation" title=" residual life estimation"> residual life estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20voltage%20plus%20capacitor" title=" high voltage plus capacitor"> high voltage plus capacitor</a> </p> <a href="https://publications.waset.org/abstracts/45882/residual-life-estimation-based-on-multi-phase-nonlinear-wiener-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45882.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">453</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">3974</span> The Beta-Fisher Snedecor Distribution with Applications to Cancer Remission Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20A.%20Adepoju">K. A. Adepoju</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20I.%20Shittu"> O. I. Shittu</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20U.%20Chukwu"> A. U. Chukwu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new four-parameter generalized version of the Fisher Snedecor distribution called Beta- F distribution is introduced. The comprehensive account of the statistical properties of the new distributions was considered. Formal expressions for the cumulative density function, moments, moment generating function and maximum likelihood estimation, as well as its Fisher information, were obtained. The flexibility of this distribution as well as its robustness using cancer remission time data was demonstrated. The new distribution can be used in most applications where the assumption underlying the use of other lifetime distributions is violated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fisher-snedecor%20distribution" title="fisher-snedecor distribution">fisher-snedecor distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=beta-f%20distribution" title=" beta-f distribution"> beta-f distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=outlier" title=" outlier"> outlier</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20method" title=" maximum likelihood method"> maximum likelihood method</a> </p> <a href="https://publications.waset.org/abstracts/46554/the-beta-fisher-snedecor-distribution-with-applications-to-cancer-remission-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46554.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">3973</span> Comparison of Methods of Estimation for Use in Goodness of Fit Tests for Binary Multilevel Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20V.%20Pinto">I. V. Pinto</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20R.%20Sooriyarachchi"> M. R. Sooriyarachchi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It can be frequently observed that the data arising in our environment have a hierarchical or a nested structure attached with the data. Multilevel modelling is a modern approach to handle this kind of data. When multilevel modelling is combined with a binary response, the estimation methods get complex in nature and the usual techniques are derived from quasi-likelihood method. The estimation methods which are compared in this study are, marginal quasi-likelihood (order 1 & order 2) (MQL1, MQL2) and penalized quasi-likelihood (order 1 & order 2) (PQL1, PQL2). A statistical model is of no use if it does not reflect the given dataset. Therefore, checking the adequacy of the fitted model through a goodness-of-fit (GOF) test is an essential stage in any modelling procedure. However, prior to usage, it is also equally important to confirm that the GOF test performs well and is suitable for the given model. This study assesses the suitability of the GOF test developed for binary response multilevel models with respect to the method used in model estimation. An extensive set of simulations was conducted using MLwiN (v 2.19) with varying number of clusters, cluster sizes and intra cluster correlations. The test maintained the desirable Type-I error for models estimated using PQL2 and it failed for almost all the combinations of MQL. Power of the test was adequate for most of the combinations in all estimation methods except MQL1. Moreover, models were fitted using the four methods to a real-life dataset and performance of the test was compared for each model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=goodness-of-fit%20test" title="goodness-of-fit test">goodness-of-fit test</a>, <a href="https://publications.waset.org/abstracts/search?q=marginal%20quasi-likelihood" title=" marginal quasi-likelihood"> marginal quasi-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=multilevel%20modelling" title=" multilevel modelling"> multilevel modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=penalized%20quasi-likelihood" title=" penalized quasi-likelihood"> penalized quasi-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=power" title=" power"> power</a>, <a href="https://publications.waset.org/abstracts/search?q=quasi-likelihood" title=" quasi-likelihood"> quasi-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=type-I%20error" title=" type-I error"> type-I error</a> </p> <a href="https://publications.waset.org/abstracts/105519/comparison-of-methods-of-estimation-for-use-in-goodness-of-fit-tests-for-binary-multilevel-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105519.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">142</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">3972</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">3971</span> Forecasting the Volatility of Geophysical Time Series with Stochastic Volatility Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maria%20C.%20Mariani">Maria C. Mariani</a>, <a href="https://publications.waset.org/abstracts/search?q=Md%20Al%20Masum%20Bhuiyan"> Md Al Masum Bhuiyan</a>, <a href="https://publications.waset.org/abstracts/search?q=Osei%20K.%20Tweneboah"> Osei K. Tweneboah</a>, <a href="https://publications.waset.org/abstracts/search?q=Hector%20G.%20Huizar"> Hector G. Huizar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work is devoted to the study of modeling geophysical time series. A stochastic technique with time-varying parameters is used to forecast the volatility of data arising in geophysics. In this study, the volatility is defined as a logarithmic first-order autoregressive process. We observe that the inclusion of log-volatility into the time-varying parameter estimation significantly improves forecasting which is facilitated via maximum likelihood estimation. This allows us to conclude that the estimation algorithm for the corresponding one-step-ahead suggested volatility (with ±2 standard prediction errors) is very feasible since it possesses good convergence properties. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Augmented%20Dickey%20Fuller%20Test" title="Augmented Dickey Fuller Test">Augmented Dickey Fuller Test</a>, <a href="https://publications.waset.org/abstracts/search?q=geophysical%20time%20series" title=" geophysical time series"> geophysical time series</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20volatility%20model" title=" stochastic volatility model"> stochastic volatility model</a> </p> <a href="https://publications.waset.org/abstracts/75110/forecasting-the-volatility-of-geophysical-time-series-with-stochastic-volatility-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75110.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">315</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">3970</span> Decision Making Approach through Generalized Fuzzy Entropy Measure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20D.%20Arora">H. D. Arora</a>, <a href="https://publications.waset.org/abstracts/search?q=Anjali%20Dhiman"> Anjali Dhiman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Uncertainty is found everywhere and its understanding is central to decision making. Uncertainty emerges as one has less information than the total information required describing a system and its environment. Uncertainty and information are so closely associated that the information provided by an experiment for example, is equal to the amount of uncertainty removed. It may be pertinent to point out that uncertainty manifests itself in several forms and various kinds of uncertainties may arise from random fluctuations, incomplete information, imprecise perception, vagueness etc. For instance, one encounters uncertainty due to vagueness in communication through natural language. Uncertainty in this sense is represented by fuzziness resulting from imprecision of meaning of a concept expressed by linguistic terms. Fuzzy set concept provides an appropriate mathematical framework for dealing with the vagueness. Both information theory, proposed by Shannon (1948) and fuzzy set theory given by Zadeh (1965) plays an important role in human intelligence and various practical problems such as image segmentation, medical diagnosis etc. Numerous approaches and theories dealing with inaccuracy and uncertainty have been proposed by different researcher. In the present communication, we generalize fuzzy entropy proposed by De Luca and Termini (1972) corresponding to Shannon entropy(1948). Further, some of the basic properties of the proposed measure were examined. We also applied the proposed measure to the real life decision making problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=entropy" title="entropy">entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20sets" title=" fuzzy sets"> fuzzy sets</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20entropy" title=" fuzzy entropy"> fuzzy entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20fuzzy%20entropy" title=" generalized fuzzy entropy"> generalized fuzzy entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title=" decision making"> decision making</a> </p> <a href="https://publications.waset.org/abstracts/26513/decision-making-approach-through-generalized-fuzzy-entropy-measure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26513.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">450</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">3969</span> Generalized Uncertainty Principle Modified Hawking Radiation in Bumblebee Gravity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sara%20Kanzi">Sara Kanzi</a>, <a href="https://publications.waset.org/abstracts/search?q=Izzet%20Sakalli"> Izzet Sakalli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The effect of Lorentz symmetry breaking (LSB) on the Hawking radiation of Schwarzschild-like black hole found in the bumblebee gravity model (SBHBGM) is studied in the framework of quantum gravity. To this end, we consider Hawking radiation spin-0 (bosons) and spin-12particles (fermions), which go in and out through the event horizon of the SBHBGM. We use the modified Klein-Gordon and Dirac equations, which are obtained from the generalized uncertainty principle (GUP) to show how Hawking radiation is affected by the GUP and LSB. In particular, we reveal that independent of the spin of the emitted particles, GUP causes a change in the Hawking temperature of the SBHBGM. Furthermore, we compute the semi-analytic greybody factors (for both bosons and fermions) of the SBHBGM. Thus, we reveal that LSB is effective on the greybody factor of the SBHBGM such that its redundancy decreases the value of the greybody factor. Our findings are graphically depicted. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bumblebee%20gravity%20model" title="bumblebee gravity model">bumblebee gravity model</a>, <a href="https://publications.waset.org/abstracts/search?q=Hawking%20radiation" title=" Hawking radiation"> Hawking radiation</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20uncertainty%20principle" title=" generalized uncertainty principle"> generalized uncertainty principle</a>, <a href="https://publications.waset.org/abstracts/search?q=Lorentz%20symmetry%20breaking" title=" Lorentz symmetry breaking"> Lorentz symmetry breaking</a> </p> <a href="https://publications.waset.org/abstracts/120400/generalized-uncertainty-principle-modified-hawking-radiation-in-bumblebee-gravity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/120400.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">136</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">3968</span> Classical and Bayesian Inference of the Generalized Log-Logistic Distribution with Applications to Survival Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdisalam%20Hassan%20Muse">Abdisalam Hassan Muse</a>, <a href="https://publications.waset.org/abstracts/search?q=Samuel%20Mwalili"> Samuel Mwalili</a>, <a href="https://publications.waset.org/abstracts/search?q=Oscar%20Ngesa"> Oscar Ngesa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A generalized log-logistic distribution with variable shapes of the hazard rate was introduced and studied, extending the log-logistic distribution by adding an extra parameter to the classical distribution, leading to greater flexibility in analysing and modeling various data types. The proposed distribution has a large number of well-known lifetime special sub-models such as; Weibull, log-logistic, exponential, and Burr XII distributions. Its basic mathematical and statistical properties were derived. The method of maximum likelihood was adopted for estimating the unknown parameters of the proposed distribution, and a Monte Carlo simulation study is carried out to assess the behavior of the estimators. The importance of this distribution is that its tendency to model both monotone (increasing and decreasing) and non-monotone (unimodal and bathtub shape) or reversed “bathtub” shape hazard rate functions which are quite common in survival and reliability data analysis. Furthermore, the flexibility and usefulness of the proposed distribution are illustrated in a real-life data set and compared to its sub-models; Weibull, log-logistic, and BurrXII distributions and other parametric survival distributions with 3-parmaeters; like the exponentiated Weibull distribution, the 3-parameter lognormal distribution, the 3- parameter gamma distribution, the 3-parameter Weibull distribution, and the 3-parameter log-logistic (also known as shifted log-logistic) distribution. The proposed distribution provided a better fit than all of the competitive distributions based on the goodness-of-fit tests, the log-likelihood, and information criterion values. Finally, Bayesian analysis and performance of Gibbs sampling for the data set are also carried out. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hazard%20rate%20function" title="hazard rate function">hazard rate function</a>, <a href="https://publications.waset.org/abstracts/search?q=log-logistic%20distribution" title=" log-logistic distribution"> log-logistic distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20log-logistic%20distribution" title=" generalized log-logistic distribution"> generalized log-logistic distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=survival%20data" title=" survival data"> survival data</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulation" title=" Monte Carlo simulation"> Monte Carlo simulation</a> </p> <a href="https://publications.waset.org/abstracts/139326/classical-and-bayesian-inference-of-the-generalized-log-logistic-distribution-with-applications-to-survival-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139326.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">202</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">3967</span> Agegraphic Dark Energy with GUP</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20R.%20Fazlollahi">H. R. Fazlollahi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dark Energy origin is unknown and so describing this mysterious component in large scale structure needs to manipulate our theories in general relativity. Although in most models, dark energy arises from extra terms through modifying Einstein-Hilbert action, maybe its origin traces back to fundamental aspects of ground energy of space-time given in quantum mechanics. Hence, diluting space-time in general relativity with quantum mechanics properties leads to the Karolyhazy relation corresponding energy density of quantum fluctuations of space-time. Through generalized uncertainty principle and an eye to Karolyhazy approach in this study we extend energy density of quantum fluctuations of space-time. Also, the application of this idea is considered in late time evolution and we have shown how extra term in generalized uncertainty principle plays as a plausible interaction term role in suggested model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20uncertainty%20principle" title="generalized uncertainty principle">generalized uncertainty principle</a>, <a href="https://publications.waset.org/abstracts/search?q=karolyhazy%20approach" title=" karolyhazy approach"> karolyhazy approach</a>, <a href="https://publications.waset.org/abstracts/search?q=agegraphic%20dark%20energy" title=" agegraphic dark energy"> agegraphic dark energy</a>, <a href="https://publications.waset.org/abstracts/search?q=cosmology" title=" cosmology"> cosmology</a> </p> <a href="https://publications.waset.org/abstracts/165000/agegraphic-dark-energy-with-gup" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165000.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">73</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">3966</span> Estimation of Uncertainty of Thermal Conductivity Measurement with Single Laboratory Validation Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saowaluck%20Ukrisdawithid">Saowaluck Ukrisdawithid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The thermal conductivity of thermal insulation materials are measured by Heat Flow Meter (HFM) apparatus. The components of uncertainty are complex and difficult on routine measurement by modelling approach. In this study, uncertainty of thermal conductivity measurement was estimated by single laboratory validation approach. The within-laboratory reproducibility was 1.1%. The standard uncertainty of method and laboratory bias by using SRM1453 expanded polystyrene board was dominant at 1.4%. However, it was assessed that there was no significant bias. For sample measurement, the sources of uncertainty were repeatability, density of sample and thermal conductivity resolution of HFM. From this approach to sample measurements, the combined uncertainty was calculated. In summary, the thermal conductivity of sample, polystyrene foam, was reported as 0.03367 W/m·K ± 3.5% (k = 2) at mean temperature 23.5 °C. The single laboratory validation approach is simple key of routine testing laboratory for estimation uncertainty of thermal conductivity measurement by using HFM, according to ISO/IEC 17025-2017 requirements. These are meaningful for laboratory competent improvement, quality control on products, and conformity assessment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=single%20laboratory%20validation%20approach" title="single laboratory validation approach">single laboratory validation approach</a>, <a href="https://publications.waset.org/abstracts/search?q=within-laboratory%20reproducibility" title=" within-laboratory reproducibility"> within-laboratory reproducibility</a>, <a href="https://publications.waset.org/abstracts/search?q=method%20and%20laboratory%20bias" title=" method and laboratory bias"> method and laboratory bias</a>, <a href="https://publications.waset.org/abstracts/search?q=certified%20reference%20material" title=" certified reference material"> certified reference material</a> </p> <a href="https://publications.waset.org/abstracts/115436/estimation-of-uncertainty-of-thermal-conductivity-measurement-with-single-laboratory-validation-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115436.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">153</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">3965</span> The Normal-Generalized Hyperbolic Secant Distribution: Properties and Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hazem%20M.%20Al-Mofleh">Hazem M. Al-Mofleh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new four-parameter univariate continuous distribution called the Normal-Generalized Hyperbolic Secant Distribution (NGHS) is defined and studied. Some general and structural distributional properties are investigated and discussed, including: central and non-central n-th moments and incomplete moments, quantile and generating functions, hazard function, Rényi and Shannon entropies, shapes: skewed right, skewed left, and symmetric, modality regions: unimodal and bimodal, maximum likelihood (MLE) estimators for the parameters. Finally, two real data sets are used to demonstrate empirically its flexibility and prove the strength of the new distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bimodality" title="bimodality">bimodality</a>, <a href="https://publications.waset.org/abstracts/search?q=estimation" title=" estimation"> estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=hazard%20function" title=" hazard function"> hazard function</a>, <a href="https://publications.waset.org/abstracts/search?q=moments" title=" moments"> moments</a>, <a href="https://publications.waset.org/abstracts/search?q=Shannon%E2%80%99s%20entropy" title=" Shannon’s entropy"> Shannon’s entropy</a> </p> <a href="https://publications.waset.org/abstracts/62567/the-normal-generalized-hyperbolic-secant-distribution-properties-and-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62567.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">348</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">3964</span> An Extension of the Generalized Extreme Value Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serge%20Provost">Serge Provost</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdous%20Saboor"> Abdous Saboor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A q-analogue of the generalized extreme value distribution which includes the Gumbel distribution is introduced. The additional parameter q allows for increased modeling flexibility. The resulting distribution can have a finite, semi-infinite or infinite support. It can also produce several types of hazard rate functions. The model parameters are determined by making use of the method of maximum likelihood. It will be shown that it compares favourably to three related distributions in connection with the modeling of a certain hydrological data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title="extreme value theory">extreme value theory</a>, <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=goodness-of-fit%20statistics" title=" goodness-of-fit statistics"> goodness-of-fit statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=Gumbel%20distribution" title=" Gumbel distribution"> Gumbel distribution</a> </p> <a href="https://publications.waset.org/abstracts/72656/an-extension-of-the-generalized-extreme-value-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72656.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">349</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">3963</span> Uncertainty Estimation in Neural Networks through Transfer Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashish%20James">Ashish James</a>, <a href="https://publications.waset.org/abstracts/search?q=Anusha%20James"> Anusha James</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its widespread use. Estimating the confidence of these predictions is paramount for improving the safety and reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to be overconfident and unreasonable. Ensemble of NNs typically produce good predictions but uncertainty estimates tend to be inconsistent. Inspired by these, this paper presents a framework that can quantitatively estimate the uncertainties by leveraging the advances in transfer learning through slight modification to the existing training pipelines. This promising algorithm is developed with an intention of deployment in real world problems which already boast a good predictive performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of experiments with known and unknown distributions show that the proposed approach produces well calibrated uncertainty estimates with high quality predictions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20estimation" title="uncertainty estimation">uncertainty estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/153501/uncertainty-estimation-in-neural-networks-through-transfer-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153501.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">135</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">3962</span> Tenants Use Less Input on Rented Plots: Evidence from Northern Ethiopia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Desta%20Brhanu%20Gebrehiwot">Desta Brhanu Gebrehiwot</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study aims to investigate the impact of land tenure arrangements on fertilizer use per hectare in Northern Ethiopia. Household and Plot level data are used for analysis. Land tenure contracts such as sharecropping and fixed rent arrangements have endogeneity. Different unobservable characteristics may affect renting-out decisions. Thus, the appropriate method of analysis was the instrumental variable estimation technic. Therefore, the family of instrumental variable estimation methods two-stage least-squares regression (2SLS, the generalized method of moments (GMM), Limited information maximum likelihood (LIML), and instrumental variable Tobit (IV-Tobit) was used. Besides, a method to handle a binary endogenous variable is applied, which uses a two-step estimation. In the first step probit model includes instruments, and in the second step, maximum likelihood estimation (MLE) (“etregress” command in Stata 14) was used. There was lower fertilizer use per hectare on sharecropped and fixed rented plots relative to owner-operated. The result supports the Marshallian inefficiency principle in sharecropping. The difference in fertilizer use per hectare could be explained by a lack of incentivized detailed contract forms, such as giving more proportion of the output to the tenant under sharecropping contracts, which motivates to use of more fertilizer in rented plots to maximize the production because most sharecropping arrangements share output equally between tenants and landlords. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tenure-contracts" title="tenure-contracts">tenure-contracts</a>, <a href="https://publications.waset.org/abstracts/search?q=endogeneity" title=" endogeneity"> endogeneity</a>, <a href="https://publications.waset.org/abstracts/search?q=plot-level%20data" title=" plot-level data"> plot-level data</a>, <a href="https://publications.waset.org/abstracts/search?q=Ethiopia" title=" Ethiopia"> Ethiopia</a>, <a href="https://publications.waset.org/abstracts/search?q=fertilizer" title=" fertilizer"> fertilizer</a> </p> <a href="https://publications.waset.org/abstracts/163031/tenants-use-less-input-on-rented-plots-evidence-from-northern-ethiopia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163031.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">86</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">3961</span> Parameter Estimation with Uncertainty and Sensitivity Analysis for the SARS Outbreak in Hong Kong</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Afia%20Naheed">Afia Naheed</a>, <a href="https://publications.waset.org/abstracts/search?q=Manmohan%20Singh"> Manmohan Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Lucy"> David Lucy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work is based on a mathematical as well as statistical study of an SEIJTR deterministic model for the interpretation of transmission of severe acute respiratory syndrome (SARS). Based on the SARS epidemic in 2003, the parameters are estimated using Runge-Kutta (Dormand-Prince pairs) and least squares methods. Possible graphical and numerical techniques are used to validate the estimates. Then effect of the model parameters on the dynamics of the disease is examined using sensitivity and uncertainty analysis. Sensitivity and uncertainty analytical techniques are used in order to analyze the affect of the uncertainty in the obtained parameter estimates and to determine which parameters have the largest impact on controlling the disease dynamics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=infectious%20disease" title="infectious disease">infectious disease</a>, <a href="https://publications.waset.org/abstracts/search?q=severe%20acute%20respiratory%20syndrome%20%28SARS%29" title=" severe acute respiratory syndrome (SARS)"> severe acute respiratory syndrome (SARS)</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20estimation" title=" parameter estimation"> parameter estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitivity%20analysis" title=" sensitivity analysis"> sensitivity analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20analysis" title=" uncertainty analysis"> uncertainty analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Runge-Kutta%20methods" title=" Runge-Kutta methods"> Runge-Kutta methods</a>, <a href="https://publications.waset.org/abstracts/search?q=Levenberg-Marquardt%20method" title=" Levenberg-Marquardt method"> Levenberg-Marquardt method</a> </p> <a href="https://publications.waset.org/abstracts/8087/parameter-estimation-with-uncertainty-and-sensitivity-analysis-for-the-sars-outbreak-in-hong-kong" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8087.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">361</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">3960</span> Donoho-Stark’s and Hardy’s Uncertainty Principles for the Short-Time Quaternion Offset Linear Canonical Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Younus%20Bhat">Mohammad Younus Bhat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The quaternion offset linear canonical transform (QOLCT), which isa time-shifted and frequency-modulated version of the quaternion linear canonical transform (QLCT), provides a more general framework of most existing signal processing tools. For the generalized QOLCT, the classical Heisenberg’s and Lieb’s uncertainty principles have been studied recently. In this paper, we first define the short-time quaternion offset linear canonical transform (ST-QOLCT) and drive its relationship with the quaternion Fourier transform (QFT). The crux of the paper lies in the generalization of several well-known uncertainty principles for the ST-QOLCT, including Donoho-Stark’s uncertainty principle, Hardy’s uncertainty principle, Beurling’s uncertainty principle, and the logarithmic uncertainty principle. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Quaternion%20Fourier%20transform" title="Quaternion Fourier transform">Quaternion Fourier transform</a>, <a href="https://publications.waset.org/abstracts/search?q=Quaternion%20offset%20linear%20canonical%20transform" title=" Quaternion offset linear canonical transform"> Quaternion offset linear canonical transform</a>, <a href="https://publications.waset.org/abstracts/search?q=short-time%20quaternion%20offset%20linear%20canonical%20transform" title=" short-time quaternion offset linear canonical transform"> short-time quaternion offset linear canonical transform</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20principle" title=" uncertainty principle"> uncertainty principle</a> </p> <a href="https://publications.waset.org/abstracts/142375/donoho-starks-and-hardys-uncertainty-principles-for-the-short-time-quaternion-offset-linear-canonical-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142375.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">211</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">3959</span> Determination of Measurement Uncertainty of the Diagnostic Meteorological Model CALMET</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nina%20Miklav%C4%8Di%C4%8D">Nina Miklavčič</a>, <a href="https://publications.waset.org/abstracts/search?q=Ur%C5%A1ka%20Kugovnik"> Urška Kugovnik</a>, <a href="https://publications.waset.org/abstracts/search?q=Natalia%20Galkina"> Natalia Galkina</a>, <a href="https://publications.waset.org/abstracts/search?q=Primo%C5%BE%20Ribari%C4%8D"> Primož Ribarič</a>, <a href="https://publications.waset.org/abstracts/search?q=Rudi%20Von%C4%8Dina"> Rudi Vončina</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today, the need for weather predictions is deeply rooted in the everyday life of people as well as it is in industry. The forecasts influence final decision-making processes in multiple areas, from agriculture and prevention of natural disasters to air traffic regulations and solutions on a national level for health, security, and economic problems. Namely, in Slovenia, alongside other existing forms of application, weather forecasts are adopted for the prognosis of electrical current transmission through powerlines. Meteorological parameters are one of the key factors which need to be considered in estimations of the reliable supply of electrical energy to consumers. And like for any other measured value, the knowledge about measurement uncertainty is also critical for the secure and reliable supply of energy. The estimation of measurement uncertainty grants us a more accurate interpretation of data, a better quality of the end results, and even a possibility of improvement of weather forecast models. In the article, we focused on the estimation of measurement uncertainty of the diagnostic microscale meteorological model CALMET. For the purposes of our research, we used a network of meteorological stations spread in the area of our interest, which enables a side-by-side comparison of measured meteorological values with the values calculated with the help of CALMET and the measurement uncertainty estimation as a final result. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=uncertancy" title="uncertancy">uncertancy</a>, <a href="https://publications.waset.org/abstracts/search?q=meteorological%20model" title=" meteorological model"> meteorological model</a>, <a href="https://publications.waset.org/abstracts/search?q=meteorological%20measurment" title=" meteorological measurment"> meteorological measurment</a>, <a href="https://publications.waset.org/abstracts/search?q=CALMET" title=" CALMET"> CALMET</a> </p> <a href="https://publications.waset.org/abstracts/171084/determination-of-measurement-uncertainty-of-the-diagnostic-meteorological-model-calmet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171084.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">81</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">3958</span> Exponentiated Transmuted Weibull Distribution: A Generalization of the Weibull Probability Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abd%20El%20Hady%20N.%20Ebraheim">Abd El Hady N. Ebraheim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces a new generalization of the two parameter Weibull distribution. To this end, the quadratic rank transmutation map has been used. This new distribution is named exponentiated transmuted Weibull (ETW) distribution. The ETW distribution has the advantage of being capable of modeling various shapes of aging and failure criteria. Furthermore, eleven lifetime distributions such as the Weibull, exponentiated Weibull, Rayleigh and exponential distributions, among others follow as special cases. The properties of the new model are discussed and the maximum likelihood estimation is used to estimate the parameters. Explicit expressions are derived for the quantiles. The moments of the distribution are derived, and the order statistics are examined. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exponentiated" title="exponentiated">exponentiated</a>, <a href="https://publications.waset.org/abstracts/search?q=inversion%20method" title=" inversion method"> inversion method</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=transmutation%20map" title=" transmutation map"> transmutation map</a> </p> <a href="https://publications.waset.org/abstracts/3470/exponentiated-transmuted-weibull-distribution-a-generalization-of-the-weibull-probability-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3470.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">565</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">3957</span> Implications of Climate Change and World Uncertainty for Gender Inequality: Global Evidence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kashif%20Nesar%20Rather">Kashif Nesar Rather</a>, <a href="https://publications.waset.org/abstracts/search?q=Mantu%20Kumar%20Mahalik"> Mantu Kumar Mahalik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The discourse surrounding climate change has gained considerable traction, with a discernible emphasis on its nuanced and consequential impact on gender inequality. Concurrently, escalating global tensions are contributing to heightened uncertainty, potentially exerting influence on gender disparities. Within this framework, this study attempts to empirically investigate the implications of climate change and world uncertainty on the gender inequality for a balanced panel of 100 economies between 1995 to 2021. The estimated models also control for the effects of globalisation, economic growth, and education expenditure. The panel cointegration tests establish a significant long-run relationship between the variables of the study. Furthermore, the PMG-ARDL (Panel mean group-Autoregressive distributed lag model) estimation technique confirms that both climate change and world uncertainty perpetuate the global gender inequalities. Additionally, the results establish that globalisation, economic growth, and education expenditure exert a mitigating influence on gender inequality, signifying their role in diminishing gender disparities. These findings are further confirmed by the FGLS (Feasible Generalized Least Squares) and DKSE (Driscoll-Kraay Standard Errors) regression methods. Potential policy implications for mitigating the detrimental gender ramifications stemming from climate change and rising world uncertainties are also discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gender%20inequality" title="gender inequality">gender inequality</a>, <a href="https://publications.waset.org/abstracts/search?q=world%20uncertainty" title=" world uncertainty"> world uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=climate%20change" title=" climate change"> climate change</a>, <a href="https://publications.waset.org/abstracts/search?q=globalisation." title=" globalisation."> globalisation.</a>, <a href="https://publications.waset.org/abstracts/search?q=ecological%20footprint" title=" ecological footprint"> ecological footprint</a> </p> <a href="https://publications.waset.org/abstracts/186177/implications-of-climate-change-and-world-uncertainty-for-gender-inequality-global-evidence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186177.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">38</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">3956</span> Analysis of Financial Time Series by Using Ornstein-Uhlenbeck Type Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md%20Al%20Masum%20Bhuiyan">Md Al Masum Bhuiyan</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20C.%20Mariani"> Maria C. Mariani</a>, <a href="https://publications.waset.org/abstracts/search?q=Osei%20K.%20Tweneboah"> Osei K. Tweneboah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the present work, we develop a technique for estimating the volatility of financial time series by using stochastic differential equation. Taking the daily closing prices from developed and emergent stock markets as the basis, we argue that the incorporation of stochastic volatility into the time-varying parameter estimation significantly improves the forecasting performance via Maximum Likelihood Estimation. While using the technique, we see the long-memory behavior of data sets and one-step-ahead-predicted log-volatility with ±2 standard errors despite the variation of the observed noise from a Normal mixture distribution, because the financial data studied is not fully Gaussian. Also, the Ornstein-Uhlenbeck process followed in this work simulates well the financial time series, which aligns our estimation algorithm with large data sets due to the fact that this algorithm has good convergence properties. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=financial%20time%20series" title="financial time series">financial time series</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Ornstein-Uhlenbeck%20type%20models" title=" Ornstein-Uhlenbeck type models"> Ornstein-Uhlenbeck type models</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20volatility%20model" title=" stochastic volatility model"> stochastic volatility model</a> </p> <a href="https://publications.waset.org/abstracts/73856/analysis-of-financial-time-series-by-using-ornstein-uhlenbeck-type-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73856.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">242</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=generalized%20likelihood%20uncertainty%20estimation&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=generalized%20likelihood%20uncertainty%20estimation&page=3">3</a></li> <li class="page-item"><a class="page-link" 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