Bayesian inference under partial prior information

被引：6

作者：

Moreno, E ^{[1
]}

Bertolino, F

Racugno, W

机构：

[1] Univ Granada, Dept Stat, E-18071 Granada, Spain

[2] Univ Cagliari, I-09124 Cagliari, Italy

来源：

SCANDINAVIAN JOURNAL OF STATISTICS | 2003年 / 30卷 / 03期

关键词：

Bayesian robustness; generalized moments class; intrinsic priors; prior elicitation; quantiles; unimodality;

D O I：

10.1111/1467-9469.00349

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Partial prior information on the marginal distribution of an observable random variable is considered. When this information is incorporated into the statistical analysis of an assumed parametric model, the posterior inference is typically non-robust so that no inferential conclusion is obtained. To overcome this difficulty a method based on the standard default prior associated to the model and an intrinsic procedure is proposed. Posterior robustness of the resulting inferences is analysed and some illustrative examples are provided.

引用

页码：565 / 580

页数：16

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