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process mixtures of generalized linear model</title> <meta name="description" content="Search results for: Dirichlet process mixtures of generalized linear model"> <meta name="keywords" content="Dirichlet process mixtures of generalized linear model"> <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 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</div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Dirichlet process mixtures of generalized linear model</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">30969</span> Evaluating Traffic Congestion Using the Bayesian Dirichlet Process Mixture of Generalized Linear Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ren%20Moses">Ren Moses</a>, <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Kidando"> Emmanuel Kidando</a>, <a href="https://publications.waset.org/abstracts/search?q=Eren%20Ozguven"> Eren Ozguven</a>, <a href="https://publications.waset.org/abstracts/search?q=Yassir%20Abdelrazig"> Yassir Abdelrazig</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study applied traffic speed and occupancy to develop clustering models that identify different traffic conditions. Particularly, these models are based on the Dirichlet Process Mixture of Generalized Linear regression (DML) and change-point regression (CR). The model frameworks were implemented using 2015 historical traffic data aggregated at a 15-minute interval from an Interstate 295 freeway in Jacksonville, Florida. Using the deviance information criterion (DIC) to identify the appropriate number of mixture components, three traffic states were identified as free-flow, transitional, and congested condition. Results of the DML revealed that traffic occupancy is statistically significant in influencing the reduction of traffic speed in each of the identified states. Influence on the free-flow and the congested state was estimated to be higher than the transitional flow condition in both evening and morning peak periods. Estimation of the critical speed threshold using CR revealed that 47 mph and 48 mph are speed thresholds for congested and transitional traffic condition during the morning peak hours and evening peak hours, respectively. Free-flow speed thresholds for morning and evening peak hours were estimated at 64 mph and 66 mph, respectively. The proposed approaches will facilitate accurate detection and prediction of traffic congestion for developing effective countermeasures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic%20congestion" title="traffic congestion">traffic congestion</a>, <a href="https://publications.waset.org/abstracts/search?q=multistate%20speed%20distribution" title=" multistate speed distribution"> multistate speed distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20occupancy" title=" traffic occupancy"> traffic occupancy</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20process%20mixtures%20of%20generalized%20linear%20model" title=" Dirichlet process mixtures of generalized linear model"> Dirichlet process mixtures of generalized linear model</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20change-point%20detection" title=" Bayesian change-point detection"> Bayesian change-point detection</a> </p> <a href="https://publications.waset.org/abstracts/67198/evaluating-traffic-congestion-using-the-bayesian-dirichlet-process-mixture-of-generalized-linear-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67198.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">294</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">30968</span> Multinomial Dirichlet Gaussian Process Model for Classification of Multidimensional Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanhyun%20Cho">Wanhyun Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonja%20Kang"> Soonja Kang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanggoon%20Kim"> Sanggoon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonyoung%20Park"> Soonyoung Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present probabilistic multinomial Dirichlet classification model for multidimensional data and Gaussian process priors. Here, we have considered an efficient computational method that can be used to obtain the approximate posteriors for latent variables and parameters needed to define the multiclass Gaussian process classification model. We first investigated the process of inducing a posterior distribution for various parameters and latent function by using the variational Bayesian approximations and important sampling method, and next we derived a predictive distribution of latent function needed to classify new samples. The proposed model is applied to classify the synthetic multivariate dataset in order to verify the performance of our model. Experiment result shows that our model is more accurate than the other approximation methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multinomial%20dirichlet%20classification%20model" title="multinomial dirichlet classification model">multinomial dirichlet classification model</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20priors" title=" Gaussian process priors"> Gaussian process priors</a>, <a href="https://publications.waset.org/abstracts/search?q=variational%20Bayesian%20approximation" title=" variational Bayesian approximation"> variational Bayesian approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=importance%20sampling" title=" importance sampling"> importance sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=approximate%20posterior%20distribution" title=" approximate posterior distribution"> approximate posterior distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=marginal%20likelihood%20evidence" title=" marginal likelihood evidence"> marginal likelihood evidence</a> </p> <a href="https://publications.waset.org/abstracts/33816/multinomial-dirichlet-gaussian-process-model-for-classification-of-multidimensional-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33816.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">444</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">30967</span> Human Action Recognition Using Variational Bayesian HMM with Dirichlet Process Mixture of Gaussian Wishart Emission Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanhyun%20Cho">Wanhyun Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonja%20Kang"> Soonja Kang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sangkyoon%20Kim"> Sangkyoon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonyoung%20Park"> Soonyoung Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present the human action recognition method using the variational Bayesian HMM with the Dirichlet process mixture (DPM) of the Gaussian-Wishart emission model (GWEM). First, we define the Bayesian HMM based on the Dirichlet process, which allows an infinite number of Gaussian-Wishart components to support continuous emission observations. Second, we have considered an efficient variational Bayesian inference method that can be applied to drive the posterior distribution of hidden variables and model parameters for the proposed model based on training data. And then we have derived the predictive distribution that may be used to classify new action. Third, the paper proposes a process of extracting appropriate spatial-temporal feature vectors that can be used to recognize a wide range of human behaviors from input video image. Finally, we have conducted experiments that can evaluate the performance of the proposed method. The experimental results show that the method presented is more efficient with human action recognition than existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20action%20recognition" title="human action recognition">human action recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20HMM" title=" Bayesian HMM"> Bayesian HMM</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20process%20mixture%20model" title=" Dirichlet process mixture model"> Dirichlet process mixture model</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian-Wishart%20emission%20model" title=" Gaussian-Wishart emission model"> Gaussian-Wishart emission model</a>, <a href="https://publications.waset.org/abstracts/search?q=Variational%20Bayesian%20inference" title=" Variational Bayesian inference"> Variational Bayesian inference</a>, <a href="https://publications.waset.org/abstracts/search?q=prior%20distribution%20and%20approximate%20posterior%20distribution" title=" prior distribution and approximate posterior distribution"> prior distribution and approximate posterior distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=KTH%20dataset" title=" KTH dataset"> KTH dataset</a> </p> <a href="https://publications.waset.org/abstracts/49713/human-action-recognition-using-variational-bayesian-hmm-with-dirichlet-process-mixture-of-gaussian-wishart-emission-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49713.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">30966</span> Learning the Dynamics of Articulated Tracked Vehicles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mario%20Gianni">Mario Gianni</a>, <a href="https://publications.waset.org/abstracts/search?q=Manuel%20A.%20Ruiz%20Garcia"> Manuel A. Ruiz Garcia</a>, <a href="https://publications.waset.org/abstracts/search?q=Fiora%20Pirri"> Fiora Pirri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we present a Bayesian non-parametric approach to model the motion control of ATVs. The motion control model is based on a Dirichlet Process-Gaussian Process (DP-GP) mixture model. The DP-GP mixture model provides a flexible representation of patterns of control manoeuvres along trajectories of different lengths and discretizations. The model also estimates the number of patterns, sufficient for modeling the dynamics of the ATV. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20processes" title="Dirichlet processes">Dirichlet processes</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20mixture%20models" title=" gaussian mixture models"> gaussian mixture models</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20motion%20patterns" title=" learning motion patterns"> learning motion patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=tracked%20robots%20for%20urban%20search%20and%20rescue" title=" tracked robots for urban search and rescue"> tracked robots for urban search and rescue</a> </p> <a href="https://publications.waset.org/abstracts/45613/learning-the-dynamics-of-articulated-tracked-vehicles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45613.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">449</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">30965</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">30964</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">30963</span> Investigation on Machine Tools Energy Consumptions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shiva%20Abdoli">Shiva Abdoli</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20T.Semere"> Daniel T.Semere</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Several researches have been conducted to study consumption of energy in cutting process. Most of these researches are focusing to measure the consumption and propose consumption reduction methods. In this work, the relation between the cutting parameters and the consumption is investigated in order to establish a generalized energy consumption model that can be used for process and production planning in real production lines. Using the generalized model, the process planning will be carried out by taking into account the energy as a function of the selected process parameters. Similarly, the generalized model can be used in production planning to select the right operational parameters like batch sizes, routing, buffer size, etc. in a production line. The description and derivation of the model as well as a case study are given in this paper to illustrate the applicability and validity of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20parameters" title="process parameters">process parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=cutting%20process" title=" cutting process"> cutting process</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20efficiency" title=" energy efficiency"> energy efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=Material%20Removal%20Rate%20%28MRR%29" title=" Material Removal Rate (MRR) "> Material Removal Rate (MRR) </a> </p> <a href="https://publications.waset.org/abstracts/10303/investigation-on-machine-tools-energy-consumptions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10303.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">499</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">30962</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">30961</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">30960</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">30959</span> Closed Forms of Trigonometric Series Interms of Riemann’s ζ Function and Dirichlet η, λ, β Functions or the Hurwitz Zeta Function and Harmonic Numbers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Slobodan%20B.%20Tri%C4%8Dkovi%C4%87">Slobodan B. Tričković</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present the results concerned with trigonometric series that include sine and cosine functions with a parameter appearing in the denominator. We derive two types of closed-form formulas for trigonometric series. At first, for some integer values, as we know that Riemann’s ζ function and Dirichlet η, λ equal zero at negative even integers, whereas Dirichlet’s β function equals zero at negative odd integers, after a certain number of members, the rest of the series vanishes. Thus, a trigonometric series becomes a polynomial with coefficients involving Riemann’s ζ function and Dirichlet η, λ, β functions. On the other hand, in some cases, one cannot immediately replace the parameter with any positive integer because we shall encounter singularities. So it is necessary to take a limit, so in the process, we apply L’Hospital’s rule and, after a series of rearrangements, we bring a trigonometric series to a form suitable for the application of Choi-Srivastava’s theorem dealing with Hurwitz’s zeta function and Harmonic numbers. In this way, we express a trigonometric series as a polynomial over Hurwitz’s zeta function derivative. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20eta%20lambda%20beta%20functions" title="Dirichlet eta lambda beta functions">Dirichlet eta lambda beta functions</a>, <a href="https://publications.waset.org/abstracts/search?q=Riemann%27s%20zeta%20function" title=" Riemann's zeta function"> Riemann's zeta function</a>, <a href="https://publications.waset.org/abstracts/search?q=Hurwitz%20zeta%20function" title=" Hurwitz zeta function"> Hurwitz zeta function</a>, <a href="https://publications.waset.org/abstracts/search?q=Harmonic%20numbers" title=" Harmonic numbers"> Harmonic numbers</a> </p> <a href="https://publications.waset.org/abstracts/167649/closed-forms-of-trigonometric-series-interms-of-riemanns-z-function-and-dirichlet-i-l-v-functions-or-the-hurwitz-zeta-function-and-harmonic-numbers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167649.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">103</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">30958</span> Existence of Positive Solutions to a Dirichlet Second Order Boundary Value Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Sufian%20Jusoh">Muhammad Sufian Jusoh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mesliza%20Mohamed"> Mesliza Mohamed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we investigate the existence of positive solutions for a Dirichlet second order boundary value problem by applying the Krasnosel'skii fixed point theorem on compression and expansion of cones. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Krasnosel%27skii%20fixed%20point%20theorem" title="Krasnosel'skii fixed point theorem">Krasnosel'skii fixed point theorem</a>, <a href="https://publications.waset.org/abstracts/search?q=positive%20solutions" title=" positive solutions"> positive solutions</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20boundary%20value%20problem" title=" Dirichlet boundary value problem"> Dirichlet boundary value problem</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20second%20order%20%20boundary%20problem" title=" Dirichlet second order boundary problem"> Dirichlet second order boundary problem</a> </p> <a href="https://publications.waset.org/abstracts/16347/existence-of-positive-solutions-to-a-dirichlet-second-order-boundary-value-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16347.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">418</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">30957</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">30956</span> An Infinite Mixture Model for Modelling Stutter Ratio in Forensic Data Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20C.%20S.%20Sampath%20Fernando">M. A. C. S. Sampath Fernando</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20M.%20Curran"> James M. Curran</a>, <a href="https://publications.waset.org/abstracts/search?q=Renate%20Meyer"> Renate Meyer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forensic DNA analysis has received much attention over the last three decades, due to its incredible usefulness in human identification. The statistical interpretation of DNA evidence is recognised as one of the most mature fields in forensic science. Peak heights in an Electropherogram (EPG) are approximately proportional to the amount of template DNA in the original sample being tested. A stutter is a minor peak in an EPG, which is not masking as an allele of a potential contributor, and considered as an artefact that is presumed to be arisen due to miscopying or slippage during the PCR. Stutter peaks are mostly analysed in terms of stutter ratio that is calculated relative to the corresponding parent allele height. Analysis of mixture profiles has always been problematic in evidence interpretation, especially with the presence of PCR artefacts like stutters. Unlike binary and semi-continuous models; continuous models assign a probability (as a continuous weight) for each possible genotype combination, and significantly enhances the use of continuous peak height information resulting in more efficient reliable interpretations. Therefore, the presence of a sound methodology to distinguish between stutters and real alleles is essential for the accuracy of the interpretation. Sensibly, any such method has to be able to focus on modelling stutter peaks. Bayesian nonparametric methods provide increased flexibility in applied statistical modelling. Mixture models are frequently employed as fundamental data analysis tools in clustering and classification of data and assume unidentified heterogeneous sources for data. In model-based clustering, each unknown source is reflected by a cluster, and the clusters are modelled using parametric models. Specifying the number of components in finite mixture models, however, is practically difficult even though the calculations are relatively simple. Infinite mixture models, in contrast, do not require the user to specify the number of components. Instead, a Dirichlet process, which is an infinite-dimensional generalization of the Dirichlet distribution, is used to deal with the problem of a number of components. Chinese restaurant process (CRP), Stick-breaking process and Pólya urn scheme are frequently used as Dirichlet priors in Bayesian mixture models. In this study, we illustrate an infinite mixture of simple linear regression models for modelling stutter ratio and introduce some modifications to overcome weaknesses associated with CRP. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chinese%20restaurant%20process" title="Chinese restaurant process">Chinese restaurant process</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20prior" title=" Dirichlet prior"> Dirichlet prior</a>, <a href="https://publications.waset.org/abstracts/search?q=infinite%20mixture%20model" title=" infinite mixture model"> infinite mixture model</a>, <a href="https://publications.waset.org/abstracts/search?q=PCR%20stutter" title=" PCR stutter"> PCR stutter</a> </p> <a href="https://publications.waset.org/abstracts/57612/an-infinite-mixture-model-for-modelling-stutter-ratio-in-forensic-data-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57612.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">330</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">30955</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">30954</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">30953</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">30952</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">30951</span> Topic Modelling Using Latent Dirichlet Allocation and Latent Semantic Indexing on SA Telco Twitter Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Phumelele%20Kubheka">Phumelele Kubheka</a>, <a href="https://publications.waset.org/abstracts/search?q=Pius%20Owolawi"> Pius Owolawi</a>, <a href="https://publications.waset.org/abstracts/search?q=Gbolahan%20Aiyetoro"> Gbolahan Aiyetoro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Twitter is one of the most popular social media platforms where users can share their opinions on different subjects. As of 2010, The Twitter platform generates more than 12 Terabytes of data daily, ~ 4.3 petabytes in a single year. For this reason, Twitter is a great source for big mining data. Many industries such as Telecommunication companies can leverage the availability of Twitter data to better understand their markets and make an appropriate business decision. This study performs topic modeling on Twitter data using Latent Dirichlet Allocation (LDA). The obtained results are benchmarked with another topic modeling technique, Latent Semantic Indexing (LSI). The study aims to retrieve topics on a Twitter dataset containing user tweets on South African Telcos. Results from this study show that LSI is much faster than LDA. However, LDA yields better results with higher topic coherence by 8% for the best-performing model represented in Table 1. A higher topic coherence score indicates better performance of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=latent%20Dirichlet%20allocation" title=" latent Dirichlet allocation"> latent Dirichlet allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=latent%20semantic%20indexing" title=" latent semantic indexing"> latent semantic indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=telco" title=" telco"> telco</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20modeling" title=" topic modeling"> topic modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=twitter" title=" twitter"> twitter</a> </p> <a href="https://publications.waset.org/abstracts/147818/topic-modelling-using-latent-dirichlet-allocation-and-latent-semantic-indexing-on-sa-telco-twitter-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147818.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">150</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">30950</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">30949</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">30948</span> A Non-Standard Finite Difference Scheme for the Solution of Laplace Equation with Dirichlet Boundary Conditions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Moaddy">Khaled Moaddy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a fast and accurate numerical scheme for the solution of a Laplace equation with Dirichlet boundary conditions. The non-standard finite difference scheme (NSFD) is applied to construct the numerical solutions of a Laplace equation with two different Dirichlet boundary conditions. The solutions obtained using NSFD are compared with the solutions obtained using the standard finite difference scheme (SFD). The NSFD scheme is demonstrated to be reliable and efficient. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=standard%20finite%20difference%20schemes" title="standard finite difference schemes">standard finite difference schemes</a>, <a href="https://publications.waset.org/abstracts/search?q=non-standard%20schemes" title=" non-standard schemes"> non-standard schemes</a>, <a href="https://publications.waset.org/abstracts/search?q=Laplace%20equation" title=" Laplace equation"> Laplace equation</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20boundary%20conditions" title=" Dirichlet boundary conditions"> Dirichlet boundary conditions</a> </p> <a href="https://publications.waset.org/abstracts/119718/a-non-standard-finite-difference-scheme-for-the-solution-of-laplace-equation-with-dirichlet-boundary-conditions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119718.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">132</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">30947</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">30946</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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">30945</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">30944</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">30943</span> Soil-Geopolymer Mixtures for Pavement Base and Subbase Layers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Khattak">Mohammad Khattak</a>, <a href="https://publications.waset.org/abstracts/search?q=Bikash%20Adhikari"> Bikash Adhikari</a>, <a href="https://publications.waset.org/abstracts/search?q=Sambodh%20Adhikari"> Sambodh Adhikari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research deals with the physical, microstructural, mechanical, and shrinkage characteristics of flyash-based soil-geopolymer mixtures. Medium and high plastic soils were obtained from local construction projects. Class F flyash was used with a mixture of sodium silicate and sodium hydroxide solution to develop soil-geopolymer mixtures. Several mixtures were compacted, cured at different curing conditions, and tested for unconfined compressive strength (UCS), linear shrinkage, and observed under scanning electron microscopy (SEM). The results of the study demonstrated that the soil-geopolymer mixtures fulfilled the UCS criteria of cement treated design (CTD) and cement stabilized design (CSD) as recommended by the department of transportation for pavement base and subbase layers. It was found that soil-geopolymer demonstrated either similar or better UCS and shrinkage characteristics relative to conventional soil-cement mixtures. The SEM analysis revealed that microstructure of soil-geopolymer mixtures exhibited development and steady growth of geopolymerization during the curing period. Based on mechanical, shrinkage, and microstructural characteristics it was suggested that the soil-geopolymer mixtures, has an immense potential to be used as pavement subgrade, subbase, and base layers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=soil-geopolymer" title="soil-geopolymer">soil-geopolymer</a>, <a href="https://publications.waset.org/abstracts/search?q=pavement%20base" title=" pavement base"> pavement base</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20stabilization" title=" soil stabilization"> soil stabilization</a>, <a href="https://publications.waset.org/abstracts/search?q=unconfined%20compressive%20strength" title=" unconfined compressive strength"> unconfined compressive strength</a>, <a href="https://publications.waset.org/abstracts/search?q=shrinkage" title=" shrinkage"> shrinkage</a>, <a href="https://publications.waset.org/abstracts/search?q=microstructure" title=" microstructure"> microstructure</a>, <a href="https://publications.waset.org/abstracts/search?q=and%20morphology" title=" and morphology"> and morphology</a> </p> <a href="https://publications.waset.org/abstracts/91879/soil-geopolymer-mixtures-for-pavement-base-and-subbase-layers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91879.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">194</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">30942</span> A Generalized Model for Performance Analysis of Airborne Radar in Clutter Scenario</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vinod%20Kumar%20Jaysaval">Vinod Kumar Jaysaval</a>, <a href="https://publications.waset.org/abstracts/search?q=Prateek%20Agarwal"> Prateek Agarwal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Performance prediction of airborne radar is a challenging and cumbersome task in clutter scenario for different types of targets. A generalized model requires to predict the performance of Radar for air targets as well as ground moving targets. In this paper, we propose a generalized model to bring out the performance of airborne radar for different Pulsed Repetition Frequency (PRF) as well as different type of targets. The model provides a platform to bring out different subsystem parameters for different applications and performance requirements under different types of clutter terrain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=airborne%20radar" title="airborne radar">airborne radar</a>, <a href="https://publications.waset.org/abstracts/search?q=blind%20zone" title=" blind zone"> blind zone</a>, <a href="https://publications.waset.org/abstracts/search?q=clutter" title=" clutter"> clutter</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20of%20detection" title=" probability of detection"> probability of detection</a> </p> <a href="https://publications.waset.org/abstracts/13998/a-generalized-model-for-performance-analysis-of-airborne-radar-in-clutter-scenario" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13998.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">470</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">30941</span> On the Grid Technique by Approximating the Derivatives of the Solution of the Dirichlet Problems for (1+1) Dimensional Linear Schrodinger Equation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lawrence%20A.%20Farinola">Lawrence A. Farinola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Four point implicit schemes for the approximation of the first and pure second order derivatives for the solution of the Dirichlet problem for one dimensional Schrodinger equation with respect to the time variable t were constructed. Also, special four-point implicit difference boundary value problems are proposed for the first and pure second derivatives of the solution with respect to the spatial variable x. The Grid method is also applied to the mixed second derivative of the solution of the Linear Schrodinger time-dependent equation. It is assumed that the initial function belongs to the Holder space C⁸⁺ᵃ, 0 < α < 1, the Schrodinger wave function given in the Schrodinger equation is from the Holder space Cₓ,ₜ⁶⁺ᵃ, ³⁺ᵃ/², the boundary functions are from C⁴⁺ᵃ, and between the initial and the boundary functions the conjugation conditions of orders q = 0,1,2,3,4 are satisfied. It is proven that the solution of the proposed difference schemes converges uniformly on the grids of the order O(h²+ k) where h is the step size in x and k is the step size in time. Numerical experiments are illustrated to support the analysis made. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=approximation%20of%20derivatives" title="approximation of derivatives">approximation of derivatives</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20difference%20method" title=" finite difference method"> finite difference method</a>, <a href="https://publications.waset.org/abstracts/search?q=Schr%C3%B6dinger%20equation" title=" Schrödinger equation"> Schrödinger equation</a>, <a href="https://publications.waset.org/abstracts/search?q=uniform%20error" title=" uniform error"> uniform error</a> </p> <a href="https://publications.waset.org/abstracts/99442/on-the-grid-technique-by-approximating-the-derivatives-of-the-solution-of-the-dirichlet-problems-for-11-dimensional-linear-schrodinger-equation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99442.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">121</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">30940</span> Detecting Earnings Management via Statistical and Neural Networks Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Namazi">Mohammad Namazi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Sadeghzadeh%20Maharluie"> Mohammad Sadeghzadeh Maharluie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting earnings management is vital for the capital market participants, financial analysts and managers. The aim of this research is attempting to respond to this query: Is there a significant difference between the regression model and neural networks’ models in predicting earnings management, and which one leads to a superior prediction of it? In approaching this question, a Linear Regression (LR) model was compared with two neural networks including Multi-Layer Perceptron (MLP), and Generalized Regression Neural Network (GRNN). The population of this study includes 94 listed companies in Tehran Stock Exchange (TSE) market from 2003 to 2011. After the results of all models were acquired, ANOVA was exerted to test the hypotheses. In general, the summary of statistical results showed that the precision of GRNN did not exhibit a significant difference in comparison with MLP. In addition, the mean square error of the MLP and GRNN showed a significant difference with the multi variable LR model. These findings support the notion of nonlinear behavior of the earnings management. Therefore, it is more appropriate for capital market participants to analyze earnings management based upon neural networks techniques, and not to adopt linear regression models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=earnings%20management" title="earnings management">earnings management</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20linear%20regression" title=" generalized linear regression"> generalized linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks%20multi-layer%20perceptron" title=" neural networks multi-layer perceptron"> neural networks multi-layer perceptron</a>, <a href="https://publications.waset.org/abstracts/search?q=Tehran%20stock%20exchange" title=" Tehran stock exchange"> Tehran stock exchange</a> </p> <a href="https://publications.waset.org/abstracts/29730/detecting-earnings-management-via-statistical-and-neural-networks-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29730.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">421</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=Dirichlet%20process%20mixtures%20of%20generalized%20linear%20model&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Dirichlet%20process%20mixtures%20of%20generalized%20linear%20model&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Dirichlet%20process%20mixtures%20of%20generalized%20linear%20model&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Dirichlet%20process%20mixtures%20of%20generalized%20linear%20model&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Dirichlet%20process%20mixtures%20of%20generalized%20linear%20model&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Dirichlet%20process%20mixtures%20of%20generalized%20linear%20model&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Dirichlet%20process%20mixtures%20of%20generalized%20linear%20model&page=8">8</a></li> <li class="page-item"><a class="page-link" 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