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| 引用本文： | ����,�ε���. �������ֲ������¶�Ԫ����ģ���к����������[J]. 应用概率统计, 2017, 33(1): 21-31 |
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| 作者姓名： | ���� �ε��� |
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| 作者单位： | ??????????????, ??????????????? |
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| 摘 要： |
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| 关 键 词： | ??????? ???????? Gibbs???? Metropolis-Hasting???? |
Improper and Proper Posteriors with Improper Priors in Multivariate Linear Model |
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| HE Lei,HE DaoJiang. Improper and Proper Posteriors with Improper Priors in Multivariate Linear Model[J]. Chinese Journal of Applied Probability and Statisties, 2017, 33(1): 21-31 |
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| Authors: | HE Lei HE DaoJiang |
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| Affiliation: | Department of Statistics, Anhui Normal University, College of Mathematics and Science, Shanghai Normal University |
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| Abstract: | In Bayesian analysis, the Markov Chain Monte Carlo (MCMC)algorithm is an efficient and simple method to compute posteriors. However, thechain may appear to converge while the posterior is improper, which will leadsto incorrect statistical inferences. In this paper, we focus on the necessary andsufficient conditions for which improper hierarchical priors can yield properposteriors in a multivariate linear model. In addition, we carry out a simulationstudy to illustrate the theoretical results, in which the Gibbs sampling andMetropolis-Hasting sampling are employed to generate the posteriors. |
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| Keywords: | hierarchical priors proper posteriors Gibbs sampling Metropolis-Hasting sampling |
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