Issues in Robustness Analysis

被引：0

作者：

Goldstein, Michael ^{[1
]}

机构：

[1] Univ Durham, Dept Math Sci, Stat, Sci Labs, Stockton Rd, Durham DH1 3LE, England

来源：

STATISTICAL SCIENCE | 2016年 / 31卷 / 04期

关键词：

Robustness; axiomatics; coherence;

D O I：

10.1214/16-STS563

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：

How may we develop methods of analysis which address the consequences of the mismatch between the formal structural requirements of Bayesian analysis and the actual assessments that are carried out in practice? A paper by Watson and Holmes provides an overview of methods developed to address such issues and makes suggestions as to how such analyses might be carried out. This article adds commentary on the principles and practices which should guide us in such problems.

引用

页码：503 / 505

页数：3

共 3 条

[1] Reified Bayesian modelling and inference for physical systems [J].

Goldstein, Michael ;

Rougier, Jonathan .

JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2009, 139 (03) :1221-1239

[2] Approximate Models and Robust Decisions [J].

Watson, James ;

Holmes, Chris .

STATISTICAL SCIENCE, 2016, 31 (04) :465-489

[3] Posterior Belief Assessment: Extracting Meaningful Subjective Judgements from Bayesian Analyses with Complex Statistical Models [J].

Williamson, Daniel ;

Goldstein, Michael .

BAYESIAN ANALYSIS, 2015, 10 (04) :877-908

← 1 →