Bayesian Computation Statistics
Rashin Nimaei; Farzad Eskandari
Abstract
The recent advancements in technology have faced an increase in the growth rate of data.According to the amount of data generated, ensuring effective analysis using traditional approaches becomes very complicated.One of the methods of managing and analyzing big data ...
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The recent advancements in technology have faced an increase in the growth rate of data.According to the amount of data generated, ensuring effective analysis using traditional approaches becomes very complicated.One of the methods of managing and analyzing big data is classification.%One of the data mining methods used commonly and effectively to classify big data is the MapReduceIn this paper, the feature weighting technique to improve Bayesian classification algorithms for big data is developed based on Correlative Naive Bayes classifier and MapReduce Model.%Classification models include Naive Bayes classifier, correlated Naive Bayes and correlated Naive Bayes with feature weighting.Correlated Naive Bayes classification is a generalization of the Naive Bayes classification model by considering the dependence between features.%This paper uses the feature weighting technique and Laplace calibration to improve the correlated Naive Bayes classification.The performance of all described methods are evaluated by considering accuracy, sensitivity and specificity, accuracy, sensitivity and specificity metrics.
Bayesian Computation Statistics
Ehsan Ormoz; Farzad Eskandari
Abstract
This paper introduces a novel semiparametric Bayesian approach for bivariate meta-regression. The method extends traditional binomial models to trinomial distributions, accounting for positive, neutral, and negative treatment effects. Using a conditional Dirichlet process, we develop a model to compare ...
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This paper introduces a novel semiparametric Bayesian approach for bivariate meta-regression. The method extends traditional binomial models to trinomial distributions, accounting for positive, neutral, and negative treatment effects. Using a conditional Dirichlet process, we develop a model to compare treatment and control groups across multiple clinical centers. This approach addresses the challenges posed by confounding factors in such studies. The primary objective is to assess treatment efficacy by modeling response outcomes as trinomial distributions. We employ Gibbs sampling and the Metropolis-Hastings algorithm for posterior computation. These methods generate estimates of treatment effects while incorporating auxiliary variables that may influence outcomes. Simulations across various scenarios demonstrate the model’s effectiveness. We also establish credible intervals to evaluate hypotheses related to treatment effects. Furthermore, we apply the methodology to real-world data on economic activity in Iran from 2009 to 2021. This application highlights the practical utility of our approach in meta-analytic contexts. Our research contributes to the growing body of literature on Bayesian methods in meta-analysis. It provides valuable insights for improving clinical study evaluations.
Bayesian Computation Statistics
Mahdieh Bayati
Abstract
This study generalizes the joint empirical likelihood (JEL) which is named the joint penalized empirical likelihood(JPEL) and presents a comparative analysis of two innovative empirical likelihood methods: the restricted penalized empirical likelihood (RPEL) and the joint penalized empirical likelihood. ...
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This study generalizes the joint empirical likelihood (JEL) which is named the joint penalized empirical likelihood(JPEL) and presents a comparative analysis of two innovative empirical likelihood methods: the restricted penalized empirical likelihood (RPEL) and the joint penalized empirical likelihood. These methods extend traditional empirical likelihood approaches by integrating criteria based on the minimum variance and unbiasedness of the estimator equations. In RPEL, estimators are obtained under these two criteria, while JPEL facilitates the joint application of the estimator equations used in RPEL, allowing for broader applicability.\\We evaluate the effectiveness of RPEL and RJEL in regression models through simulation studies, and evaluate the performance of RPEL and JPEL, focusing on parameter accuracy, model selection (as measured by the Empirical Bayesian Information Criterion), predictive accuracy (Mean Square Error), and robustness to outliers. Results indicate that RPEL consistently outperforms JPEL across all criteria, with RPEL yielding simpler models and more reliable estimates, particularly as sample sizes increase. These findings suggest that RPEL provides greater stability and interpretability for regression models, making it a superior choice over JPEL for the scenarios tested in this study.
Bayesian Computation Statistics
Iman Makhdoom; Shahram Yaghoobzadeh Shahrastani; FGhazalnaz Sharifonnasabi
Abstract
This study focuses on estimating the parameters of the Lindley distribution under a Type-II censoringscheme using Bayesian inference. Three estimation approaches—E-Bayesian, hierarchical Bayesian, andBayesian methods—are employed, with a focus on vague prior data. The accuracy of the estimates ...
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This study focuses on estimating the parameters of the Lindley distribution under a Type-II censoringscheme using Bayesian inference. Three estimation approaches—E-Bayesian, hierarchical Bayesian, andBayesian methods—are employed, with a focus on vague prior data. The accuracy of the estimates isevaluated using the entropy loss function and the squared error loss function (SELF). We assess theefficiency of the proposed methods through Monte Carlo simulations, utilizing the Lindley approximationand the Markov Chain Monte Carlo (MCMC) technique. To demonstrate its practical applicability, weapply the methodology to a real-world dataset to analyze the performance of the methods in detail.Comparative results from the simulations and data analysis reveal the robustness and accuracy of theproposed approaches. This comprehensive evaluation underscores the advantages of Bayesian methods inparameter estimation under censoring schemes, providing valuable insights for applications in reliabilityanalysis and related fields. The study concludes with a summary of key findings, offering a foundation forfurther exploration of Bayesian techniques in censored data analysis.
Bayesian Computation Statistics
Iman Makhdoom
Abstract
This article focuses on the M/M/ 1 /K queuing model. In this model, the inter-arrival times ofcustomers to the system are random variables with an exponential distribution parameterized by λ , andthe service times of customers are random variables with an exponential distribution parameterized ...
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This article focuses on the M/M/ 1 /K queuing model. In this model, the inter-arrival times ofcustomers to the system are random variables with an exponential distribution parameterized by λ , andthe service times of customers are random variables with an exponential distribution parameterized byµ . We aim to estimate the traffic intensity parameter of this model using Bayesian, E-Bayesian, andhierarchical Bayesian methods. These methods utilize the entropy loss function and an appropriate priordistribution for the independent parameters λ and µ . Additionally, we employ the shrinkage-basedmaximum likelihood estimation method to obtain the parameter estimates. To determine the desiredtraffic intensity parameter estimate, we introduce a decision criterion based on a cost function, anda fuzzy criterion called the Average Customer Satisfaction Index (ACSI). The goal is to select theestimation with a higher ACSI index. To facilitate understanding, we compare this estimation using theMonte Carlo simulation method and two numerical examples based on the ACSI index.
Bayesian Computation Statistics
Ehsan Ormoz
Abstract
In the meta-analysis of clinical trials, usually the data of each trail summarized by one or more outcome measure estimates which reported along with their standard errors. In the case that summary data are multi-dimensional, usually, the data analysis will be performed in the form of a number of separated ...
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In the meta-analysis of clinical trials, usually the data of each trail summarized by one or more outcome measure estimates which reported along with their standard errors. In the case that summary data are multi-dimensional, usually, the data analysis will be performed in the form of a number of separated univariate analysis. In such a case the correlation between summary statistics would be ignored. In contrast, a multivariate meta-analysis model, use from these correlations synthesizes the outcomes, jointly to estimate the multiple pooled effects simultaneously. In this paper, we present a nonparametric Bayesian bivariate random effect meta-analysis.
Bayesian Computation Statistics
sima naghizadeh
Abstract
The Bayesian variable selection analysis is widely used as a new methodology in air quality control trials and generalized linear models. One of the important and, of course, controversial topics in this area is selection of prior distribution of unknown model parameters. The aim of this study is presenting ...
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The Bayesian variable selection analysis is widely used as a new methodology in air quality control trials and generalized linear models. One of the important and, of course, controversial topics in this area is selection of prior distribution of unknown model parameters. The aim of this study is presenting a substitution for mixture of priors which besidespreservation of benefits and computational efficiencies obviate the available paradoxes and contradictions. In this research we pay attention to two points of view; empirical and fully Bayesian. Especially, a mixture of priors and its theoretical characteristics is introduced. Finally, the proposed model is illustrated with a real example.
