On the use of marginal posteriors in marginal likelihood estimation via importance sampling

被引：54
|
作者：
Perrakis, Konstantinos [1 ]
Ntzoufras, Ioannis [1 ]
Tsionas, Efthymios G. [2 ]
机构：
[1] Athens Univ Econ & Business, Dept Stat, Athens, Greece
[2] Athens Univ Econ & Business, Dept Econ, Athens, Greece
来源：
COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2014年 / 77卷
关键词：
Finite normal mixtures; Importance sampling; Marginal posterior; Marginal likelihood estimation; Random effect models; Rao-Blackwellization; APPROXIMATE BAYESIAN-INFERENCE; NORMALIZING CONSTANTS; MIXTURE-MODELS; SIMULATION; DISTRIBUTIONS; SELECTION;
D O I：
10.1016/j.csda.2014.03.004
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The efficiency of a marginal likelihood estimator where the product of the marginal posterior distributions is used as an importance sampling function is investigated. The approach is generally applicable to multi-block parameter vector settings, does not require additional Markov Chain Monte Carlo (MCMC) sampling and is not dependent on the type of MCMC scheme used to sample from the posterior. The proposed approach is applied to normal regression models, finite normal mixtures and longitudinal Poisson models, and leads to accurate marginal likelihood estimates. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码：54 / 69
页数：16
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