Robabeh Hosseinpour Samim Mamaghani; Farzad Eskandari
Abstract
In this paper, we considered a Bayesian hierarchical method using the hyper product inverse moment prior in the ultrahigh-dimensional generalized linear model (UDGLM), that was useful in the Bayesian variable selection. We showed the posterior probabilities of the true model converge to 1 as the sample ...
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In this paper, we considered a Bayesian hierarchical method using the hyper product inverse moment prior in the ultrahigh-dimensional generalized linear model (UDGLM), that was useful in the Bayesian variable selection. We showed the posterior probabilities of the true model converge to 1 as the sample size increases. For computing the posterior probabilities, we implemented the Laplace approximation. The Simpli ed Shotgun Stochastic Search with Screening (S5) procedure for generalized linear model was suggested for exploring the posterior space. Simulation studies and real data analysis using the Bayesian ultrahigh-dimensional generalized linear model indicate that the proposed method had better performance than the previous models. Keywords: Ultrahigh dimensional; Nonlocal prior; Optimal
Sajad Nezamdoust; Farzad Eskandari
Abstract
The paper considers the problem of estimation of the parameters in nite mixture models.In this article, a new method is proposed for of estimation of the parameters in nite mixture models. Traditionally, the parameter estimation in nite mixture models is performed from a likelihood ...
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The paper considers the problem of estimation of the parameters in nite mixture models.In this article, a new method is proposed for of estimation of the parameters in nite mixture models. Traditionally, the parameter estimation in nite mixture models is performed from a likelihood point of view by exploiting the expectation maximization (EM) method and the Least Square Principle. Ridge regression is an alternative to the ordinary least squares method when multicollinearity presents among the regressor variables in multiple linear regression analysis. Accordingly, we propose a new shrinkage ridge estimation approach. Based on this principle, we propose an iterative algorithm called RidgeIterative Weighted least Square (RIWLS) to estimate the parameters. Monte-Carlo simulation studies are conducted to appraise the performance of our method. The results show that the Proposed estimator perform better than the IWLS method.
