Bayesian prediction intervals and their relationship to tolerance intervals

被引：29

作者：

Hamada, M

Johnson, V

Moore, LM

Wendelberger, J

机构：

[1] Los Alamos Natl Lab, Los Alamos, NM 87545 USA

[2] Univ Michigan, Dept Biostat, Ann Arbor, MI 48105 USA

来源：

TECHNOMETRICS | 2004年 / 46卷 / 04期

关键词：

gamma distribution; Gibbs sampling; hierarchical linear model; Markov chain Monte Carlo; normal distribution; predictive density; system reliability; variance components model;

D O I：

10.1198/004017004000000518

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：

We consider Bayesian prediction intervals that contain a proportion of a finite number of observations with a specified probability. Such intervals arise in numerous applied contexts and are closely related to tolerance intervals. Several examples are provided to illustrate this methodology, and simulation studies are used to demonstrate potential pitfalls of using tolerance intervals when prediction intervals are required.

引用

页码：452 / 459

页数：8

共 25 条

[1]

AITCHISON J, 1966, J R STAT SOC B, V28, P57

[2]

AITCHISON J, 1965, BIOMETRIKA, V52, P469, DOI 10.1093/biomet/52.3-4.469

[3]

AITCHISON J, 1964, J ROY STAT SOC B, V26, P161

[4]

[Anonymous], 1993, Predictive Inference