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In recent years, the literature in the area of Bayesian asymptotics has been rapidly growing. It is increasingly important to understand the concept of posterior consistency and validate specific Bayesian methods, in terms of consistency of posterior distributions. In this paper, we build up some conceptual issues in consistency of posterior distributions,… (More)
We investigate the asymptotic behavior of the Bayes factor for regression problems in which observations are not required to be independent and identically distributed and provide general results about consistency of the Bayes factor. Then we specialize our results to the model selection problem in the context of partially linear regression model in which… (More)
Mixture models provide a method of modeling a complex probability distribution in terms of simpler structures. In particular, the method of mixture of regressions has received considerable attention due to its modeling flexibility and availability of convenient computational algorithms. While the theoretical justification has been successfully worked out… (More)
We propose a new partial correlation approach using Gaussian copula. Our empirical study found that the Gaussian copula partial correlation has the same value as that which is obtained by performing a Pearson's partial correlation. With the proposed method, based on canonical vine and d-vine, we captured direct interactions among eight histone genes.
Regression analysis is a methodology for studying the relationship between two sets of variables. It includes many statistical techniques for modeling and analyzing different types of observed data to explain the relationship between a dependent variable and a set of independent variables. Recently, there has been a dramatic increase in the development of… (More)