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
收藏
页码：2821 / 2841
页数：21
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