ON BARTLETT ADJUSTMENTS FOR APPROXIMATE BAYESIAN-INFERENCE

被引:26
作者
DICICCIO, TJ
STERN, SE
机构
[1] Department of Statistics, Stanford University, Stanford
关键词
ASYMPTOTIC EXPANSION; BARTLETT ADJUSTMENT FACTOR; CHI-SQUARED APPROXIMATION; EXPONENTIAL REGRESSION MODEL; HIGHEST POSTERIOR DENSITY REGION; LIKELIHOOD RATIO; MAXIMUM LIKELIHOOD STATISTIC; NORMAL REGRESSION MODEL; POSTERIOR RATIO; SCORE STATISTIC;
D O I
10.1093/biomet/80.4.731
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
In wide generality, the posterior distributions of the likelihood ratio statistic and the posterior ratio statistic are chi-squared to error of order O(n(-1)), where n is sample size. The error in the chi-squared approximation can be reduced to order O(n(-2)) by Bartlett correction. In this paper, explicit formulae are derived for the Bartlett adjustment factors of both statistics, and the derivations are based on the Tierney, Kass & Kadane (1989) asymptotic approximation for marginal posterior probability density functions. The use of numerical differentiation to facilitate calculation of the Bartlett adjustments is also described. Some applications are considered that concern inference about regression models from both complete and right-censored data.
引用
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页码:731 / 740
页数:10
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