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linear mixed model (GLMM)</title> <meta name="description" content="Search results for: generalized linear mixed model (GLMM)"> <meta name="keywords" content="generalized linear mixed model (GLMM)"> <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 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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="generalized linear mixed model (GLMM)"> <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> 21320</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: generalized linear mixed model (GLMM)</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21320</span> Risk Factors for Defective Autoparts Products Using Bayesian Method in Poisson Generalized Linear Mixed Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pitsanu%20Tongkhow">Pitsanu Tongkhow</a>, <a href="https://publications.waset.org/abstracts/search?q=Pichet%20Jiraprasertwong"> Pichet Jiraprasertwong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research investigates risk factors for defective products in autoparts factories. Under a Bayesian framework, a generalized linear mixed model (GLMM) in which the dependent variable, the number of defective products, has a Poisson distribution is adopted. Its performance is compared with the Poisson GLM under a Bayesian framework. The factors considered are production process, machines, and workers. The products coded RT50 are observed. The study found that the Poisson GLMM is more appropriate than the Poisson GLM. For the production Process factor, the highest risk of producing defective products is Process 1, for the Machine factor, the highest risk is Machine 5, and for the Worker factor, the highest risk is Worker 6. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=defective%20autoparts%20products" title="defective autoparts products">defective autoparts products</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20framework" title=" Bayesian framework"> Bayesian framework</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20linear%20mixed%20model%20%28GLMM%29" title=" generalized linear mixed model (GLMM)"> generalized linear mixed model (GLMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20factors" title=" risk factors "> risk factors </a> </p> <a href="https://publications.waset.org/abstracts/10195/risk-factors-for-defective-autoparts-products-using-bayesian-method-in-poisson-generalized-linear-mixed-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10195.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">570</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">21319</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">21318</span> Spatio-Temporal Analysis and Mapping of Malaria in Thailand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Krisada%20Lekdee">Krisada Lekdee</a>, <a href="https://publications.waset.org/abstracts/search?q=Sunee%20Sammatat"> Sunee Sammatat</a>, <a href="https://publications.waset.org/abstracts/search?q=Nittaya%20Boonsit"> Nittaya Boonsit</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a GLMM with spatial and temporal effects for malaria data in Thailand. A Bayesian method is used for parameter estimation via Gibbs sampling MCMC. A conditional autoregressive (CAR) model is assumed to present the spatial effects. The temporal correlation is presented through the covariance matrix of the random effects. The malaria quarterly data have been extracted from the Bureau of Epidemiology, Ministry of Public Health of Thailand. The factors considered are rainfall and temperature. The result shows that rainfall and temperature are positively related to the malaria morbidity rate. The posterior means of the estimated morbidity rates are used to construct the malaria maps. The top 5 highest morbidity rates (per 100,000 population) are in Trat (Q3, 111.70), Chiang Mai (Q3, 104.70), Narathiwat (Q4, 97.69), Chiang Mai (Q2, 88.51), and Chanthaburi (Q3, 86.82). According to the DIC criterion, the proposed model has a better performance than the GLMM with spatial effects but without temporal terms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20method" title="Bayesian method">Bayesian method</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20linear%20mixed%20model%20%28GLMM%29" title=" generalized linear mixed model (GLMM)"> generalized linear mixed model (GLMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=malaria" title=" malaria"> malaria</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20effects" title=" spatial effects"> spatial effects</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20correlation" title=" temporal correlation"> temporal correlation</a> </p> <a href="https://publications.waset.org/abstracts/10300/spatio-temporal-analysis-and-mapping-of-malaria-in-thailand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10300.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">454</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">21317</span> Reliability Prediction of Tires Using Linear Mixed-Effects Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Myung%20Hwan%20Na">Myung Hwan Na</a>, <a href="https://publications.waset.org/abstracts/search?q=Ho-%20Chun%20Song"> Ho- Chun Song</a>, <a href="https://publications.waset.org/abstracts/search?q=EunHee%20Hong"> EunHee Hong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We widely use normal linear mixed-effects model to analysis data in repeated measurement. In case of detecting heteroscedasticity and the non-normality of the population distribution at the same time, normal linear mixed-effects model can give improper result of analysis. To achieve more robust estimation, we use heavy tailed linear mixed-effects model which gives more exact and reliable analysis conclusion than standard normal linear mixed-effects model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reliability" title="reliability">reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=tires" title=" tires"> tires</a>, <a href="https://publications.waset.org/abstracts/search?q=field%20data" title=" field data"> field data</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20mixed-effects%20model" title=" linear mixed-effects model"> linear mixed-effects model</a> </p> <a href="https://publications.waset.org/abstracts/37815/reliability-prediction-of-tires-using-linear-mixed-effects-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37815.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">564</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">21316</span> Statistical Analysis of the Impact of Maritime Transport Gross Domestic Product (GDP) on Nigeria’s Economy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kehinde%20Peter%20Oyeduntan">Kehinde Peter Oyeduntan</a>, <a href="https://publications.waset.org/abstracts/search?q=Kayode%20Oshinubi"> Kayode Oshinubi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nigeria is referred as the ‘Giant of Africa’ due to high population, land mass and large economy. However, it still trails far behind many smaller economies in the continent in terms of maritime operations. As we have seen that the maritime industry is the spark plug for national growth, because it houses the most crucial infrastructure that generates wealth for a nation, it is worrisome that a nation with six seaports lag in maritime activities. In this research, we have studied how the Gross Domestic Product (GDP) of the maritime transport influences the Nigerian economy. To do this, we applied Simple Linear Regression (SLR), Support Vector Machine (SVM), Polynomial Regression Model (PRM), Generalized Additive Model (GAM) and Generalized Linear Mixed Model (GLMM) to model the relationship between the nation’s Total GDP (TGDP) and the Maritime Transport GDP (MGDP) using a time series data of 20 years. The result showed that the MGDP is statistically significant to the Nigerian economy. Amongst the statistical tool applied, the PRM of order 4 describes the relationship better when compared to other methods. The recommendations presented in this study will guide policy makers and help improve the economy of Nigeria in terms of its GDP. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=maritime%20transport" title="maritime transport">maritime transport</a>, <a href="https://publications.waset.org/abstracts/search?q=economy" title=" economy"> economy</a>, <a href="https://publications.waset.org/abstracts/search?q=GDP" title=" GDP"> GDP</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a>, <a href="https://publications.waset.org/abstracts/search?q=port" title=" port"> port</a> </p> <a href="https://publications.waset.org/abstracts/156882/statistical-analysis-of-the-impact-of-maritime-transport-gross-domestic-product-gdp-on-nigerias-economy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156882.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">21315</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">21314</span> A Mixed Integer Linear Programming Model for Flexible Job Shop Scheduling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Ziaee">Mohsen Ziaee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a mixed integer linear programming (MILP) model is presented to solve the flexible job shop scheduling problem (FJSP). This problem is one of the hardest combinatorial problems. The objective considered is the minimization of the makespan. The computational results of the proposed MILP model were compared with those of the best known mathematical model in the literature in terms of the computational time. The results show that our model has better performance with respect to all the considered performance measures including relative percentage deviation (RPD) value, number of constraints, and total number of variables. By this improved mathematical model, larger FJS problems can be optimally solved in reasonable time, and therefore, the model would be a better tool for the performance evaluation of the approximation algorithms developed for the problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=scheduling" title="scheduling">scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20job%20shop" title=" flexible job shop"> flexible job shop</a>, <a href="https://publications.waset.org/abstracts/search?q=makespan" title=" makespan"> makespan</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20integer%20linear%20programming" title=" mixed integer linear programming"> mixed integer linear programming</a> </p> <a href="https://publications.waset.org/abstracts/92281/a-mixed-integer-linear-programming-model-for-flexible-job-shop-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92281.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">186</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">21313</span> Application of a Generalized Additive Model to Reveal the Relations between the Density of Zooplankton with Other Variables in the West Daya Bay, China</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Weiwen%20Li">Weiwen Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Hao%20Huang"> Hao Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chengmao%20You"> Chengmao You</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianji%20Liao"> Jianji Liao</a>, <a href="https://publications.waset.org/abstracts/search?q=Lei%20Wang"> Lei Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Lina%20An"> Lina An</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Zooplankton are a central issue in the ecology which makes a great contribution to maintaining the balance of an ecosystem. It is critical in promoting the material cycle and energy flow within the ecosystems. A generalized additive model (GAM) was applied to analyze the relationships between the density (individuals per m³) of zooplankton and other variables in West Daya Bay. All data used in this analysis (the survey month, survey station (longitude and latitude), the depth of the water column, the superficial concentration of chlorophyll a, the benthonic concentration of chlorophyll a, the number of zooplankton species and the number of zooplankton species) were collected through monthly scientific surveys during January to December 2016. GLM model (generalized linear model) was used to choose the significant variables’ impact on the density of zooplankton, and the GAM was employed to analyze the relationship between the density of zooplankton and the significant variables. The results showed that the density of zooplankton increased with an increase of the benthonic concentration of chlorophyll a, but decreased with a decrease in the depth of the water column. Both high numbers of zooplankton species and the overall total number of zooplankton individuals led to a higher density of zooplankton. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=density" title="density">density</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20linear%20model" title=" generalized linear model"> generalized linear model</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20additive%20model" title=" generalized additive model"> generalized additive model</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20West%20Daya%20Bay" title=" the West Daya Bay"> the West Daya Bay</a>, <a href="https://publications.waset.org/abstracts/search?q=zooplankton" title=" zooplankton"> zooplankton</a> </p> <a href="https://publications.waset.org/abstracts/103582/application-of-a-generalized-additive-model-to-reveal-the-relations-between-the-density-of-zooplankton-with-other-variables-in-the-west-daya-bay-china" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103582.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">151</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">21312</span> Analysis of Risk Factors Affecting the Motor Insurance Pricing with Generalized Linear Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Puttharapong%20Sakulwaropas">Puttharapong Sakulwaropas</a>, <a href="https://publications.waset.org/abstracts/search?q=Uraiwan%20%20Jaroengeratikun"> Uraiwan Jaroengeratikun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Casualty insurance business, the optimal premium pricing and adequate cost for an insurance company are important in risk management. Normally, the insurance pure premium can be determined by multiplying the claim frequency with the claim cost. The aim of this research was to study in the application of generalized linear models to select the risk factor for model of claim frequency and claim cost for estimating a pure premium. In this study, the data set was the claim of comprehensive motor insurance, which was provided by one of the insurance company in Thailand. The results of this study found that the risk factors significantly related to pure premium at the 0.05 level consisted of no claim bonus (NCB) and used of the car (Car code). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20linear%20models" title="generalized linear models">generalized linear models</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20factor" title=" risk factor"> risk factor</a>, <a href="https://publications.waset.org/abstracts/search?q=pure%20premium" title=" pure premium"> pure premium</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20model" title=" regression model"> regression model</a> </p> <a href="https://publications.waset.org/abstracts/65636/analysis-of-risk-factors-affecting-the-motor-insurance-pricing-with-generalized-linear-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65636.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">466</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">21311</span> Spatial Time Series Models for Rice and Cassava Yields Based on Bayesian Linear Mixed Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Panudet%20Saengseedam">Panudet Saengseedam</a>, <a href="https://publications.waset.org/abstracts/search?q=Nanthachai%20Kantanantha"> Nanthachai Kantanantha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20method" title="Bayesian method">Bayesian method</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20mixed%20model" title=" linear mixed model"> linear mixed model</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20conditional%20autoregressive%20model" title=" multivariate conditional autoregressive model"> multivariate conditional autoregressive model</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20time%20series" title=" spatial time series"> spatial time series</a> </p> <a href="https://publications.waset.org/abstracts/11875/spatial-time-series-models-for-rice-and-cassava-yields-based-on-bayesian-linear-mixed-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11875.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">21310</span> Generalized Additive Model for Estimating Propensity Score</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tahmidul%20Islam">Tahmidul Islam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Propensity Score Matching (PSM) technique has been widely used for estimating causal effect of treatment in observational studies. One major step of implementing PSM is estimating the propensity score (PS). Logistic regression model with additive linear terms of covariates is most used technique in many studies. Logistics regression model is also used with cubic splines for retaining flexibility in the model. However, choosing the functional form of the logistic regression model has been a question since the effectiveness of PSM depends on how accurately the PS been estimated. In many situations, the linearity assumption of linear logistic regression may not hold and non-linear relation between the logit and the covariates may be appropriate. One can estimate PS using machine learning techniques such as random forest, neural network etc for more accuracy in non-linear situation. In this study, an attempt has been made to compare the efficacy of Generalized Additive Model (GAM) in various linear and non-linear settings and compare its performance with usual logistic regression. GAM is a non-parametric technique where functional form of the covariates can be unspecified and a flexible regression model can be fitted. In this study various simple and complex models have been considered for treatment under several situations (small/large sample, low/high number of treatment units) and examined which method leads to more covariate balance in the matched dataset. It is found that logistic regression model is impressively robust against inclusion quadratic and interaction terms and reduces mean difference in treatment and control set equally efficiently as GAM does. GAM provided no significantly better covariate balance than logistic regression in both simple and complex models. The analysis also suggests that larger proportion of controls than treatment units leads to better balance for both of the methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accuracy" title="accuracy">accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=covariate%20balances" title=" covariate balances"> covariate balances</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20additive%20model" title=" generalized additive model"> generalized additive model</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=non-linearity" title=" non-linearity"> non-linearity</a>, <a href="https://publications.waset.org/abstracts/search?q=propensity%20score%20matching" title=" propensity score matching"> propensity score matching</a> </p> <a href="https://publications.waset.org/abstracts/40433/generalized-additive-model-for-estimating-propensity-score" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40433.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">21309</span> Thyroid Stimulating Hormone Is a Biomarker for Stress: A Prospective Longitudinal Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeonghun%20Lee">Jeonghun Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Thyroid-stimulating hormone (TSH) is regulated by the negative feedback of T3 and T4 but is affected by cortisol and cytokines during allostasis. Hence, TSH levels can be influenced by stress through cortisol. In the present study, changes in TSH levels under stress and the potential of TSH as a stress marker were examined in patients lacking T3 or T4 feedback after thyroid surgery. The three stress questionnaires (Korean version of the Daily Stress Inventory, Social Readjustment Rating Scale, and Stress Overload Scale-Short [SOSS]), open-ended question (OQ), and thyroid function tests were performed twice in 106 patients enrolled from January 2019 to October 2020. Statistical analysis was performed using the generalized linear mixed effect model (GLMM) in R software version 4.1.0. In a multiple LMM involving 106 patients, T3, T4, SOSS (category), open-ended questions, the extent of thyroidectomy, and preoperative TSH were significantly correlated with lnTSH. T3 and T4 increased by 1 and lnTSH decreased by 0.03, 3.52, respectively. In case of a stressful event on OQ, lnTSH increased by 1.55. In the high-risk group, lnTSH increased by 0.79, compared with the low group (p<0.05). TSH had a significant relationship with stress, together with T3, T4, and the extent of thyroidectomy. As such, it has the potential to be used as a stress marker, though it showed a low correlation with other stress questionnaires. To address this limitation, questionnaires on various social environments and research on copy strategies are necessary for future studies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stress" title="stress">stress</a>, <a href="https://publications.waset.org/abstracts/search?q=surgery" title=" surgery"> surgery</a>, <a href="https://publications.waset.org/abstracts/search?q=thyroid%20stimulating%20hormone" title=" thyroid stimulating hormone"> thyroid stimulating hormone</a>, <a href="https://publications.waset.org/abstracts/search?q=thyroidectomy" title=" thyroidectomy"> thyroidectomy</a> </p> <a href="https://publications.waset.org/abstracts/151408/thyroid-stimulating-hormone-is-a-biomarker-for-stress-a-prospective-longitudinal-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151408.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">91</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">21308</span> A Coordinated School Health Program Effect on Cardiorespiratory Fitness in Preschool Children</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zasha%20Romero">Zasha Romero</a>, <a href="https://publications.waset.org/abstracts/search?q=Roberto%20Trevino"> Roberto Trevino</a>, <a href="https://publications.waset.org/abstracts/search?q=Lin%20Wang"> Lin Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Elizabeth%20Alanis"> Elizabeth Alanis</a>, <a href="https://publications.waset.org/abstracts/search?q=Jesus%20Cuellar"> Jesus Cuellar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: There is a strong relationship between low cardiorespiratory fitness (CRF) and high adiposity levels. The purpose of this study was to assess the effects of the Bienestar/Neema Coordinated School Health Program (BN CSHP) on the CRF of preschool children. Methods: This is a randomized cluster trial conducted in preschools of two school districts located along the Texas-Mexico border. Of 48 eligible schools, 28 were randomly selected (intervention, n=14; control, n=14). Family demographics and household health characteristics were collected from parents. CRF, as measured by the Progressive Anaerobic Capacity Endurance Run (PACER) fitness test, was collected from the children. A generalized linear mixed model (GLMM) was used to analyze the data. Results: Family demographics, household health characteristics, and children’s weight, obesity prevalence, and sedentary activity were similar among both treatment groups. After adjusting for covariates, the number of laps run by children in the control group increased by 23% (CI: -5% to 60%) per each data collection period compared with 53% (CI: 7% to 119%) in the intervention group. Conclusions: Children in the BN CSHP, compared to those in the control group, had a significantly higher increase in their CRF. This finding is important because of the health benefits of CRF in children. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coordinated%20school%20health%20program" title="coordinated school health program">coordinated school health program</a>, <a href="https://publications.waset.org/abstracts/search?q=cardiorespiratory%20fitness" title=" cardiorespiratory fitness"> cardiorespiratory fitness</a>, <a href="https://publications.waset.org/abstracts/search?q=obesity" title=" obesity"> obesity</a>, <a href="https://publications.waset.org/abstracts/search?q=border%20health" title=" border health"> border health</a>, <a href="https://publications.waset.org/abstracts/search?q=preschool" title=" preschool"> preschool</a>, <a href="https://publications.waset.org/abstracts/search?q=physical%20education" title=" physical education"> physical education</a>, <a href="https://publications.waset.org/abstracts/search?q=movement" title=" movement"> movement</a> </p> <a href="https://publications.waset.org/abstracts/163073/a-coordinated-school-health-program-effect-on-cardiorespiratory-fitness-in-preschool-children" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163073.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">85</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">21307</span> Supervised-Component-Based Generalised Linear Regression with Multiple Explanatory Blocks: THEME-SCGLR</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bry%20X.">Bry X.</a>, <a href="https://publications.waset.org/abstracts/search?q=Trottier%20C."> Trottier C.</a>, <a href="https://publications.waset.org/abstracts/search?q=Mortier%20F."> Mortier F.</a>, <a href="https://publications.waset.org/abstracts/search?q=Cornu%20G."> Cornu G.</a>, <a href="https://publications.waset.org/abstracts/search?q=Verron%20T."> Verron T.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We address component-based regularization of a Multivariate Generalized Linear Model (MGLM). A set of random responses Y is assumed to depend, through a GLM, on a set X of explanatory variables, as well as on a set T of additional covariates. X is partitioned into R conceptually homogeneous blocks X1, ... , XR , viewed as explanatory themes. Variables in each Xr are assumed many and redundant. Thus, Generalised Linear Regression (GLR) demands regularization with respect to each Xr. By contrast, variables in T are assumed selected so as to demand no regularization. Regularization is performed searching each Xr for an appropriate number of orthogonal components that both contribute to model Y and capture relevant structural information in Xr. We propose a very general criterion to measure structural relevance (SR) of a component in a block, and show how to take SR into account within a Fisher-scoring-type algorithm in order to estimate the model. We show how to deal with mixed-type explanatory variables. The method, named THEME-SCGLR, is tested on simulated data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Component-Model" title="Component-Model">Component-Model</a>, <a href="https://publications.waset.org/abstracts/search?q=Fisher%20Scoring%20Algorithm" title=" Fisher Scoring Algorithm"> Fisher Scoring Algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=GLM" title=" GLM"> GLM</a>, <a href="https://publications.waset.org/abstracts/search?q=PLS%20Regression" title=" PLS Regression"> PLS Regression</a>, <a href="https://publications.waset.org/abstracts/search?q=SCGLR" title=" SCGLR"> SCGLR</a>, <a href="https://publications.waset.org/abstracts/search?q=SEER" title=" SEER"> SEER</a>, <a href="https://publications.waset.org/abstracts/search?q=THEME" title=" THEME"> THEME</a> </p> <a href="https://publications.waset.org/abstracts/19061/supervised-component-based-generalised-linear-regression-with-multiple-explanatory-blocks-theme-scglr" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19061.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">396</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">21306</span> Generalized Correlation Coefficient in Genome-Wide Association Analysis of Cognitive Ability in Twins</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Afsaneh%20Mohammadnejad">Afsaneh Mohammadnejad</a>, <a href="https://publications.waset.org/abstracts/search?q=Marianne%20Nygaard"> Marianne Nygaard</a>, <a href="https://publications.waset.org/abstracts/search?q=Jan%20Baumbach"> Jan Baumbach</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuxia%20Li"> Shuxia Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Weilong%20Li"> Weilong Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Jesper%20Lund"> Jesper Lund</a>, <a href="https://publications.waset.org/abstracts/search?q=Jacob%20v.%20B.%20Hjelmborg"> Jacob v. B. Hjelmborg</a>, <a href="https://publications.waset.org/abstracts/search?q=Lene%20Christensen"> Lene Christensen</a>, <a href="https://publications.waset.org/abstracts/search?q=Qihua%20Tan"> Qihua Tan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cognitive impairment in the elderly is a key issue affecting the quality of life. Despite a strong genetic background in cognition, only a limited number of single nucleotide polymorphisms (SNPs) have been found. These explain a small proportion of the genetic component of cognitive function, thus leaving a large proportion unaccounted for. We hypothesize that one reason for this missing heritability is the misspecified modeling in data analysis concerning phenotype distribution as well as the relationship between SNP dosage and the phenotype of interest. In an attempt to overcome these issues, we introduced a model-free method based on the generalized correlation coefficient (GCC) in a genome-wide association study (GWAS) of cognitive function in twin samples and compared its performance with two popular linear regression models. The GCC-based GWAS identified two genome-wide significant (P-value < 5e-8) SNPs; rs2904650 near ZDHHC2 on chromosome 8 and rs111256489 near CD6 on chromosome 11. The kinship model also detected two genome-wide significant SNPs, rs112169253 on chromosome 4 and rs17417920 on chromosome 7, whereas no genome-wide significant SNPs were found by the linear mixed model (LME). Compared to the linear models, more meaningful biological pathways like GABA receptor activation, ion channel transport, neuroactive ligand-receptor interaction, and the renin-angiotensin system were found to be enriched by SNPs from GCC. The GCC model outperformed the linear regression models by identifying more genome-wide significant genetic variants and more meaningful biological pathways related to cognitive function. Moreover, GCC-based GWAS was robust in handling genetically related twin samples, which is an important feature in handling genetic confounding in association studies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognition" title="cognition">cognition</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20correlation%20coefficient" title=" generalized correlation coefficient"> generalized correlation coefficient</a>, <a href="https://publications.waset.org/abstracts/search?q=GWAS" title=" GWAS"> GWAS</a>, <a href="https://publications.waset.org/abstracts/search?q=twins" title=" twins"> twins</a> </p> <a href="https://publications.waset.org/abstracts/111850/generalized-correlation-coefficient-in-genome-wide-association-analysis-of-cognitive-ability-in-twins" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/111850.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">124</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">21305</span> Durrmeyer Type Modification of q-Generalized Bernstein Operators</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ruchi">Ruchi</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20M.%20Acu"> A. M. Acu</a>, <a href="https://publications.waset.org/abstracts/search?q=Purshottam%20N.%20Agrawal"> Purshottam N. Agrawal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper to introduce the Durrmeyer type modification of q-generalized-Bernstein operators which include the Bernstein polynomials in the particular α = 0. We investigate the rate of convergence by means of the Lipschitz class and the Peetre’s K-functional. Also, we define the bivariate case of Durrmeyer type modification of q-generalized-Bernstein operators and study the degree of approximation with the aid of the partial modulus of continuity and the Peetre’s K-functional. Finally, we introduce the GBS (Generalized Boolean Sum) of the Durrmeyer type modification of q- generalized-Bernstein operators and investigate the approximation of the Bögel continuous and Bögel differentiable functions with the aid of the Lipschitz class and the mixed modulus of smoothness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=B%C3%B6gel%20continuous" title="Bögel continuous">Bögel continuous</a>, <a href="https://publications.waset.org/abstracts/search?q=B%C3%B6gel%20differentiable" title=" Bögel differentiable"> Bögel differentiable</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20Boolean%20sum" title=" generalized Boolean sum"> generalized Boolean sum</a>, <a href="https://publications.waset.org/abstracts/search?q=Peetre%E2%80%99s%20K-functional" title=" Peetre’s K-functional"> Peetre’s K-functional</a>, <a href="https://publications.waset.org/abstracts/search?q=Lipschitz%20class" title=" Lipschitz class"> Lipschitz class</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20modulus%20of%20smoothness" title=" mixed modulus of smoothness"> mixed modulus of smoothness</a> </p> <a href="https://publications.waset.org/abstracts/79175/durrmeyer-type-modification-of-q-generalized-bernstein-operators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79175.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">213</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">21304</span> Explicit Iterative Scheme for Approximating a Common Solution of Generalized Mixed Equilibrium Problem and Fixed Point Problem for a Nonexpansive Semigroup in Hilbert Space</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Farid">Mohammad Farid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we introduce and study an explicit iterative method based on hybrid extragradient method to approximate a common solution of generalized mixed equilibrium problem and fixed point problem for a nonexpansive semigroup in Hilbert space. Further, we prove that the sequence generated by the proposed iterative scheme converge strongly to the common solution of generalized mixed equilibrium problem and fixed point problem for a nonexpansive semigroup. This common solution is the unique solution of a variational inequality problem and is the optimality condition for a minimization problem. The results presented in this paper are the supplement, extension and generalization of the previously known results in this area. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20mixed%20equilibrium%20problem" title="generalized mixed equilibrium problem">generalized mixed equilibrium problem</a>, <a href="https://publications.waset.org/abstracts/search?q=fixed-point%20problem" title=" fixed-point problem"> fixed-point problem</a>, <a href="https://publications.waset.org/abstracts/search?q=nonexpansive%20semigroup" title=" nonexpansive semigroup"> nonexpansive semigroup</a>, <a href="https://publications.waset.org/abstracts/search?q=variational%20inequality%20problem" title=" variational inequality problem"> variational inequality problem</a>, <a href="https://publications.waset.org/abstracts/search?q=iterative%20algorithms" title=" iterative algorithms"> iterative algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20extragradient%20method" title=" hybrid extragradient method"> hybrid extragradient method</a> </p> <a href="https://publications.waset.org/abstracts/14070/explicit-iterative-scheme-for-approximating-a-common-solution-of-generalized-mixed-equilibrium-problem-and-fixed-point-problem-for-a-nonexpansive-semigroup-in-hilbert-space" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14070.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">475</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">21303</span> Evaluation of a Piecewise Linear Mixed-Effects Model in the Analysis of Randomized Cross-over Trial</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moses%20Mwangi">Moses Mwangi</a>, <a href="https://publications.waset.org/abstracts/search?q=Geert%20Verbeke"> Geert Verbeke</a>, <a href="https://publications.waset.org/abstracts/search?q=Geert%20Molenberghs"> Geert Molenberghs</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cross-over designs are commonly used in randomized clinical trials to estimate efficacy of a new treatment with respect to a reference treatment (placebo or standard). The main advantage of using cross-over design over conventional parallel design is its flexibility, where every subject become its own control, thereby reducing confounding effect. Jones & Kenward, discuss in detail more recent developments in the analysis of cross-over trials. We revisit the simple piecewise linear mixed-effects model, proposed by Mwangi et. al, (in press) for its first application in the analysis of cross-over trials. We compared performance of the proposed piecewise linear mixed-effects model with two commonly cited statistical models namely, (1) Grizzle model; and (2) Jones & Kenward model, used in estimation of the treatment effect, in the analysis of randomized cross-over trial. We estimate two performance measurements (mean square error (MSE) and coverage probability) for the three methods, using data simulated from the proposed piecewise linear mixed-effects model. Piecewise linear mixed-effects model yielded lowest MSE estimates compared to Grizzle and Jones & Kenward models for both small (Nobs=20) and large (Nobs=600) sample sizes. It’s coverage probability were highest compared to Grizzle and Jones & Kenward models for both small and large sample sizes. A piecewise linear mixed-effects model is a better estimator of treatment effect than its two competing estimators (Grizzle and Jones & Kenward models) in the analysis of cross-over trials. The data generating mechanism used in this paper captures two time periods for a simple 2-Treatments x 2-Periods cross-over design. Its application is extendible to more complex cross-over designs with multiple treatments and periods. In addition, it is important to note that, even for single response models, adding more random effects increases the complexity of the model and thus may be difficult or impossible to fit in some cases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Evaluation" title="Evaluation">Evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=Grizzle%20model" title=" Grizzle model"> Grizzle model</a>, <a href="https://publications.waset.org/abstracts/search?q=Jones%20%26%20Kenward%20model" title=" Jones & Kenward model"> Jones & Kenward model</a>, <a href="https://publications.waset.org/abstracts/search?q=Performance%20measures" title=" Performance measures"> Performance measures</a>, <a href="https://publications.waset.org/abstracts/search?q=Simulation" title=" Simulation"> Simulation</a> </p> <a href="https://publications.waset.org/abstracts/123329/evaluation-of-a-piecewise-linear-mixed-effects-model-in-the-analysis-of-randomized-cross-over-trial" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123329.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">122</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">21302</span> Survey of Methods for Solutions of Spatial Covariance Structures and Their Limitations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Thomas%20Eghwerido">Joseph Thomas Eghwerido</a>, <a href="https://publications.waset.org/abstracts/search?q=Julian%20I.%20Mbegbu"> Julian I. Mbegbu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In modelling environment processes, we apply multidisciplinary knowledge to explain, explore and predict the Earth's response to natural human-induced environmental changes. Thus, the analysis of spatial-time ecological and environmental studies, the spatial parameters of interest are always heterogeneous. This often negates the assumption of stationarity. Hence, the dispersion of the transportation of atmospheric pollutants, landscape or topographic effect, weather patterns depends on a good estimate of spatial covariance. The generalized linear mixed model, although linear in the expected value parameters, its likelihood varies nonlinearly as a function of the covariance parameters. As a consequence, computing estimates for a linear mixed model requires the iterative solution of a system of simultaneous nonlinear equations. In other to predict the variables at unsampled locations, we need to know the estimate of the present sampled variables. The geostatistical methods for solving this spatial problem assume covariance stationarity (locally defined covariance) and uniform in space; which is not apparently valid because spatial processes often exhibit nonstationary covariance. Hence, they have globally defined covariance. We shall consider different existing methods of solutions of spatial covariance of a space-time processes at unsampled locations. This stationary covariance changes with locations for multiple time set with some asymptotic properties. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parametric" title="parametric">parametric</a>, <a href="https://publications.waset.org/abstracts/search?q=nonstationary" title=" nonstationary"> nonstationary</a>, <a href="https://publications.waset.org/abstracts/search?q=Kernel" title=" Kernel"> Kernel</a>, <a href="https://publications.waset.org/abstracts/search?q=Kriging" title=" Kriging"> Kriging</a> </p> <a href="https://publications.waset.org/abstracts/59194/survey-of-methods-for-solutions-of-spatial-covariance-structures-and-their-limitations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59194.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">255</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">21301</span> Spatial Occupation of the Wild Boar 'Sus Scrofa Algirus' in the Oasis of Southern Tunisia: The Continental Oasis of Kebili and the Coastal Oasis of Gabes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghandri%20Aida">Ghandri Aida</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The wild boar ‘Sus scrofa algirus’ is an invasive species that has a significant invasive potential allowing it to colonize the agroecosystems of southern Tunisia. In fact, these agroecosystems contain sites with high tranquility-refuge value (refuge zones) which are very attractive for this Suidae thanks to the very dense vegetation (reed beds on the outskirts of the oases and the border areas of the wadis and chotts) and the almost impenetrability for man. When this species is present in abundance, it could cause severe ecological and socio-economic damage. The present work aims to analyze the spatial distribution of this species in the oases of southern Tunisia, namely the coastal oases of Gabès and the continental oases of Kébili, using GLMMs (generalized linear mixed models). In particular, it aims to evaluate the influence of certain landscape factors and vegetation on the occurrence of this harmful species. Our results suggest that the spatial occupancy of wild boar in Tunisian oases essentially depends on proximity to the nearest roads as a repelling factor as well as irrigation, the proportion of cereal cultivation and proximity to areas of refuge as attractive factors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sus%20scrofa%20algirus" title="sus scrofa algirus">sus scrofa algirus</a>, <a href="https://publications.waset.org/abstracts/search?q=occurence" title=" occurence"> occurence</a>, <a href="https://publications.waset.org/abstracts/search?q=GLMM" title=" GLMM"> GLMM</a>, <a href="https://publications.waset.org/abstracts/search?q=oasis%20of%20southern%20tunisia" title=" oasis of southern tunisia"> oasis of southern tunisia</a> </p> <a href="https://publications.waset.org/abstracts/195002/spatial-occupation-of-the-wild-boar-sus-scrofa-algirus-in-the-oasis-of-southern-tunisia-the-continental-oasis-of-kebili-and-the-coastal-oasis-of-gabes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/195002.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">4</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">21300</span> A New Approach for Generalized First Derivative of Nonsmooth Functions Using Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Mehdi%20Mazarei">Mohammad Mehdi Mazarei</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Asghar%20Behroozpoor"> Ali Asghar Behroozpoor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we define an optimization problem corresponding to smooth and nonsmooth functions which its optimal solution is the first derivative of these functions in a domain. For this purpose, a linear programming problem corresponding to optimization problem is obtained. The optimal solution of this linear programming problem is the approximate generalized first derivative. In fact, we approximate generalized first derivative of nonsmooth functions as tailor series. We show the efficiency of our approach by some smooth and nonsmooth functions in some examples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=general%20derivative" title="general derivative">general derivative</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20programming" title=" linear programming"> linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20problem" title=" optimization problem"> optimization problem</a>, <a href="https://publications.waset.org/abstracts/search?q=smooth%20and%20nonsmooth%20functions" title=" smooth and nonsmooth functions"> smooth and nonsmooth functions</a> </p> <a href="https://publications.waset.org/abstracts/19425/a-new-approach-for-generalized-first-derivative-of-nonsmooth-functions-using-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19425.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">557</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">21299</span> Mixed Effects Models for Short-Term Load Forecasting for the Spanish Regions: Castilla-Leon, Castilla-La Mancha and Andalucia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20Senabre">C. Senabre</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Valero"> S. Valero</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Lopez"> M. Lopez</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Velasco"> E. Velasco</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Sanchez"> M. Sanchez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper focuses on an application of linear mixed models to short-term load forecasting. The challenge of this research is to improve a currently working model at the Spanish Transport System Operator, programmed by us, and based on linear autoregressive techniques and neural networks. The forecasting system currently forecasts each of the regions within the Spanish grid separately, even though the behavior of the load in each region is affected by the same factors in a similar way. A load forecasting system has been verified in this work by using the real data from a utility. In this research it has been used an integration of several regions into a linear mixed model as starting point to obtain the information from other regions. Firstly, the systems to learn general behaviors present in all regions, and secondly, it is identified individual deviation in each regions. The technique can be especially useful when modeling the effect of special days with scarce information from the past. The three most relevant regions of the system have been used to test the model, focusing on special day and improving the performance of both currently working models used as benchmark. A range of comparisons with different forecasting models has been conducted. The forecasting results demonstrate the superiority of the proposed methodology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=short-term%20load%20forecasting" title="short-term load forecasting">short-term load forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20effects%20models" title=" mixed effects models"> mixed effects models</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=mixed%20effects%20models" title=" mixed effects models"> mixed effects models</a> </p> <a href="https://publications.waset.org/abstracts/100166/mixed-effects-models-for-short-term-load-forecasting-for-the-spanish-regions-castilla-leon-castilla-la-mancha-and-andalucia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100166.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">189</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">21298</span> Convergence of Generalized Jacobi, Gauss-Seidel and Successive Overrelaxation Methods for Various Classes of Matrices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manideepa%20Saha">Manideepa Saha</a>, <a href="https://publications.waset.org/abstracts/search?q=Jahnavi%20Chakrabarty"> Jahnavi Chakrabarty</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generalized Jacobi (GJ) and Generalized Gauss-Seidel (GGS) methods are most effective than conventional Jacobi and Gauss-Seidel methods for solving linear system of equations. It is known that GJ and GGS methods converge for strictly diagonally dominant (SDD) and for M-matrices. In this paper, we study the convergence of GJ and GGS converge for symmetric positive definite (SPD) matrices, L-matrices and H-matrices. We introduce a generalization of successive overrelaxation (SOR) method for solving linear systems and discuss its convergence for the classes of SDD matrices, SPD matrices, M-matrices, L-matrices and for H-matrices. Advantages of generalized SOR method are established through numerical experiments over GJ, GGS, and SOR methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convergence" title="convergence">convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=Gauss-Seidel" title=" Gauss-Seidel"> Gauss-Seidel</a>, <a href="https://publications.waset.org/abstracts/search?q=iterative%20method" title=" iterative method"> iterative method</a>, <a href="https://publications.waset.org/abstracts/search?q=Jacobi" title=" Jacobi"> Jacobi</a>, <a href="https://publications.waset.org/abstracts/search?q=SOR" title=" SOR"> SOR</a> </p> <a href="https://publications.waset.org/abstracts/97280/convergence-of-generalized-jacobi-gauss-seidel-and-successive-overrelaxation-methods-for-various-classes-of-matrices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97280.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">189</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">21297</span> Optimal Production Planning in Aromatic Coconuts Supply Chain Based on Mixed-Integer Linear Programming</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chaimongkol%20Limpianchob">Chaimongkol Limpianchob</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work addresses the problem of production planning that arises in the production of aromatic coconuts from Samudsakhorn province in Thailand. The planning involves the forwarding of aromatic coconuts from the harvest areas to the factory, which is classified into two groups; self-owned areas and contracted areas, the decisions of aromatic coconuts flow in the plant, and addressing a question of which warehouse will be in use. The problem is formulated as a mixed-integer linear programming model within supply chain management framework. The objective function seeks to minimize the total cost including the harvesting, labor and inventory costs. Constraints on the system include the production activities in the company and demand requirements. Numerical results are presented to demonstrate the feasibility of coconuts supply chain model compared with base case. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aromatic%20coconut" title="aromatic coconut">aromatic coconut</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20management" title=" supply chain management"> supply chain management</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20planning" title=" production planning"> production planning</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed-integer%20linear%20programming" title=" mixed-integer linear programming"> mixed-integer linear programming</a> </p> <a href="https://publications.waset.org/abstracts/6619/optimal-production-planning-in-aromatic-coconuts-supply-chain-based-on-mixed-integer-linear-programming" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6619.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">460</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">21296</span> Weyl Type Theorem and the Fuglede Property</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20M.%20Rashid">M. H. M. Rashid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Given H a Hilbert space and B(H) the algebra of bounded linear operator in H, let δAB denote the generalized derivation defined by A and B. The main objective of this article is to study Weyl type theorems for generalized derivation for (A,B) satisfying a couple of Fuglede. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fuglede%20Property" title="Fuglede Property">Fuglede Property</a>, <a href="https://publications.waset.org/abstracts/search?q=Weyl%E2%80%99s%20theorem" title=" Weyl’s theorem"> Weyl’s theorem</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20derivation" title=" generalized derivation"> generalized derivation</a>, <a href="https://publications.waset.org/abstracts/search?q=Aluthge%20transform" title=" Aluthge transform"> Aluthge transform</a> </p> <a href="https://publications.waset.org/abstracts/113531/weyl-type-theorem-and-the-fuglede-property" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/113531.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">128</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">21295</span> Generalized Central Paths for Convex Programming</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li-Zhi%20Liao">Li-Zhi Liao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The central path has played the key role in the interior point method. However, the convergence of the central path may not be true even in some convex programming problems with linear constraints. In this paper, the generalized central paths are introduced for convex programming. One advantage of the generalized central paths is that the paths will always converge to some optimal solutions of the convex programming problem for any initial interior point. Some additional theoretical properties for the generalized central paths will be also reported. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=central%20path" title="central path">central path</a>, <a href="https://publications.waset.org/abstracts/search?q=convex%20programming" title=" convex programming"> convex programming</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20central%20path" title=" generalized central path"> generalized central path</a>, <a href="https://publications.waset.org/abstracts/search?q=interior%20point%20method" title=" interior point method"> interior point method</a> </p> <a href="https://publications.waset.org/abstracts/58039/generalized-central-paths-for-convex-programming" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58039.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">327</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">21294</span> Regional Hydrological Extremes Frequency Analysis Based on Statistical and Hydrological Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hadush%20Kidane%20Meresa">Hadush Kidane Meresa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The hydrological extremes frequency analysis is the foundation for the hydraulic engineering design, flood protection, drought management and water resources management and planning to utilize the available water resource to meet the desired objectives of different organizations and sectors in a country. This spatial variation of the statistical characteristics of the extreme flood and drought events are key practice for regional flood and drought analysis and mitigation management. For different hydro-climate of the regions, where the data set is short, scarcity, poor quality and insufﬁcient, the regionalization methods are applied to transfer at-site data to a region. This study aims in regional high and low flow frequency analysis for Poland River Basins. Due to high frequent occurring of hydrological extremes in the region and rapid water resources development in this basin have caused serious concerns over the flood and drought magnitude and frequencies of the river in Poland. The magnitude and frequency result of high and low flows in the basin is needed for flood and drought planning, management and protection at present and future. Hydrological homogeneous high and low flow regions are formed by the cluster analysis of site characteristics, using the hierarchical and C- mean clustering and PCA method. Statistical tests for regional homogeneity are utilized, by Discordancy and Heterogeneity measure tests. In compliance with results of the tests, the region river basin has been divided into ten homogeneous regions. In this study, frequency analysis of high and low flows using AM for high flow and 7-day minimum low flow series is conducted using six statistical distributions. The use of L-moment and LL-moment method showed a homogeneous region over entire province with Generalized logistic (GLOG), Generalized extreme value (GEV), Pearson type III (P-III), Generalized Pareto (GPAR), Weibull (WEI) and Power (PR) distributions as the regional drought and flood frequency distributions. The 95% percentile and Flow duration curves of 1, 7, 10, 30 days have been plotted for 10 stations. However, the cluster analysis performed two regions in west and east of the province where L-moment and LL-moment method demonstrated the homogeneity of the regions and GLOG and Pearson Type III (PIII) distributions as regional frequency distributions for each region, respectively. The spatial variation and regional frequency distribution of flood and drought characteristics for 10 best catchment from the whole region was selected and beside the main variable (streamflow: high and low) we used variables which are more related to physiographic and drainage characteristics for identify and delineate homogeneous pools and to derive best regression models for ungauged sites. Those are mean annual rainfall, seasonal flow, average slope, NDVI, aspect, flow length, flow direction, maximum soil moisture, elevation, and drainage order. The regional high-flow or low-flow relationship among one streamflow characteristics with (AM or 7-day mean annual low flows) some basin characteristics is developed using Generalized Linear Mixed Model (GLMM) and Generalized Least Square (GLS) regression model, providing a simple and effective method for estimation of flood and drought of desired return periods for ungauged catchments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flood" title="flood ">flood </a>, <a href="https://publications.waset.org/abstracts/search?q=drought" title=" drought"> drought</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency" title=" frequency"> frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=magnitude" title=" magnitude"> magnitude</a>, <a href="https://publications.waset.org/abstracts/search?q=regionalization" title=" regionalization"> regionalization</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic" title=" stochastic"> stochastic</a>, <a href="https://publications.waset.org/abstracts/search?q=ungauged" title=" ungauged"> ungauged</a>, <a href="https://publications.waset.org/abstracts/search?q=Poland" title=" Poland "> Poland </a> </p> <a href="https://publications.waset.org/abstracts/19374/regional-hydrological-extremes-frequency-analysis-based-on-statistical-and-hydrological-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19374.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">602</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">21293</span> On Fourier Type Integral Transform for a Class of Generalized Quotients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20S.%20Issa">A. S. Issa</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20K.%20Q.%20AL-Omari"> S. K. Q. AL-Omari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we investigate certain spaces of generalized functions for the Fourier and Fourier type integral transforms. We discuss convolution theorems and establish certain spaces of distributions for the considered integrals. The new Fourier type integral is well-defined, linear, one-to-one and continuous with respect to certain types of convergences. Many properties and an inverse problem are also discussed in some details. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Boehmian" title="Boehmian">Boehmian</a>, <a href="https://publications.waset.org/abstracts/search?q=Fourier%20integral" title=" Fourier integral"> Fourier integral</a>, <a href="https://publications.waset.org/abstracts/search?q=Fourier%20type%20integral" title=" Fourier type integral"> Fourier type integral</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20quotient" title=" generalized quotient"> generalized quotient</a> </p> <a href="https://publications.waset.org/abstracts/45947/on-fourier-type-integral-transform-for-a-class-of-generalized-quotients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45947.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">365</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">21292</span> Forecasting Electricity Spot Price with Generalized Long Memory Modeling: Wavelet and Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Souhir%20Ben%20Amor">Souhir Ben Amor</a>, <a href="https://publications.waset.org/abstracts/search?q=Heni%20Boubaker"> Heni Boubaker</a>, <a href="https://publications.waset.org/abstracts/search?q=Lotfi%20Belkacem"> Lotfi Belkacem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This aims of this paper is to forecast the electricity spot prices. First, we focus on modeling the conditional mean of the series so we adopt a generalized fractional -factor Gegenbauer process (k-factor GARMA). Secondly, the residual from the -factor GARMA model has used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using the Back Propagation learning algorithms. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has adopted, and the parameters of the k-factor GARMA-G-GARCH model has estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. The empirical results have shown that the k-factor GARMA-G-GARCH model outperform the hybrid k-factor GARMA-LLWNN model, and find it is more appropriate for forecasts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electricity%20price" title="electricity price">electricity price</a>, <a href="https://publications.waset.org/abstracts/search?q=k-factor%20GARMA" title=" k-factor GARMA"> k-factor GARMA</a>, <a href="https://publications.waset.org/abstracts/search?q=LLWNN" title=" LLWNN"> LLWNN</a>, <a href="https://publications.waset.org/abstracts/search?q=G-GARCH" title=" G-GARCH"> G-GARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a> </p> <a href="https://publications.waset.org/abstracts/75361/forecasting-electricity-spot-price-with-generalized-long-memory-modeling-wavelet-and-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75361.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">231</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">21291</span> Extension of Positive Linear Operator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manal%20Azzidani">Manal Azzidani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research consideres the extension of special functions called Positive Linear Operators. the bounded linear operator which defined from normed space to Banach space will extend to the closure of the its domain, And extend identified linear functional on a vector subspace by Hana-Banach theorem which could be generalized to the positive linear operators. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extension" title="extension">extension</a>, <a href="https://publications.waset.org/abstracts/search?q=positive%20operator" title=" positive operator"> positive operator</a>, <a href="https://publications.waset.org/abstracts/search?q=Riesz%20space" title=" Riesz space"> Riesz space</a>, <a href="https://publications.waset.org/abstracts/search?q=sublinear%20function" title=" sublinear function"> sublinear function</a> </p> <a href="https://publications.waset.org/abstracts/33220/extension-of-positive-linear-operator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33220.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">517</span> </span> </div> </div> <ul class="pagination"> <li class="page-item 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