Background and Purpose: Genomic Selection is used to select candidates for breeding programs for organisms. In this study, we use the Bayesian Model averaging (BMA) method for genomic Selection by considering the skewed error distributions. Materials and Methods: In this study, we apply the BMA method to linear regression Models with skew-normal and skew-t distributions to determine the best subset of predictors. Occam’s window and Markov-Chain Monte Carlo Model composition (MC3) were used to determine the best Model and its uncertainty. The Rice SNP-seek database was used to obtain real data, which included 152 single nucleotide polymorphisms (SNPs) with 6 phenotypes. Results: Numerical studies on simulated and real data showed that, although Occam’s window ran faster than the MC3 method, the latter method suggested better linear Models for the data with both skew-normal and skew-t error distributions. Conclusion: The MC3 method performs better than Occam’s window in identifying the linear Models with greater accuracy when dealing with skewed error distributions.

Risk identification, impact assessment, and response planning constitute three building blocks of project risk management. Correspondingly, three types of interactions could be envisioned between risks, between impacts of several risks on a portfolio component, and between several responses. While the interdependency of risks is a well-recognized issue, the other two types of interactions remain unacknowledged in the risk response planning literature. This research suggests a Bayesian belief network for Modeling portfolio risks, their impacts, and responses. There are three kinds of nodes in this network: nodes representing portfolio risks, nodes corresponding to risk impacts on each objective of each portfolio component, and nodes showing response actions. The problem is to decide which responses are to be selected. For this purpose, an optimization Model is proposed that minimizes the sum of both residual risk effects on portfolio component objectives and response implementation costs. Subsequently, a genetic algorithm is introduced to solve the Model. A simple portfolio instance is also provided to illustrate the proposed Model.

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 besides preservation 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.

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.

Introduction: The world prevalence of type 2 diabetes and its related increment mortality rate which needs high controls cost has attracted high scientific attention. Early detection of individuals who face this disease more than the others can prevent getting sick or at least reduce the disease consequences on public health. Regarding the costs and limitations of diagnostic tests, a statistical Model is presented that helps predict the time of diabetes incidence and determines its risk factors. Furthermore, this Model determines the significant predictor variables on response and considers them as Model equation parameters. Materials and Methods: In this study, 803 pre-diabetic women in the age range of more than 20 years were selected from Tehran lipid and glucose study (TLGS) to examine the predictor variables on time of diabetes incidence. They were entered into the study in the phases 1 and 2 and were followed up to the phase 4. The predictor variables Selection was performed using the Stepwise Model (SM) and the Bayesian Model Averaging (BMA). Then, the predictive discrimination was used to compare the results of both Models. The Log-rank test was performed and the Kaplan-Meier Curve was plotted. The statistical analyses were performed using R software (version 3. 1. 3). Results: The Backward Stepwise Model (BSM), the Forward Stepwise Model (FSM) and the BMA have used 9, 10 and 6 variables, respectively. Although the BMA selected predictor variables number is much lower than the SM, the prediction ability remains nearly constant. Conclusions: The BMA has averaged on the supported Models using dataset. This Model has shown nearly constant accuracy despite the Selection of lower predictor variables number in comparison to the SM.