Nonparametric goodness-of-fit

被引：16

作者：

Swartz, T

机构：

[1] Simon Fraser Univ, Dept Math & Stat, Burnaby, BC V5A 1S6, Canada

[2] Duke Univ, Inst Stat & Decis Sci, Durham, NC 27706 USA

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 1999年 / 28卷 / 12期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Monte Carlo; hypothesis testing; Dirichlet process; prior elicitation;

D O I：

10.1080/03610929908832452

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：

This paper develops an approach to testing the adequacy of both classical:and Bayesian models given sample data. An important. feature of the approach is that we are able to test the practical scientific hypothesis of whether the true underlying model is-close to some hypothesized model. The notion of closeness is based on measurement precision anti requires the introduction of a metric for which we consider the Kolmogorov distance. The approach is nonparametric in the sense that the model under the alternative hypothesis is a:Dirichlet process.

引用

页码：2821 / 2841

页数：21

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