Goodness-of-fit tests for parametric models based on biased samples

被引：1

作者：

Sun, YQ ^{[1
]}

Cui, SF ^{[1
]}

Tiwari, RC ^{[1
]}

机构：

[1] Univ N Carolina, Dept Math, Charlotte, NC 28223 USA

来源：

CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE | 2002年 / 30卷 / 03期

关键词：

censoring; consistency; Monte Carlo method; supremum test; truncation; weak convergence;

D O I：

10.2307/3316149

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：

The authors study the problem of checking the adequacy of a parametric model for a distribution using several possibly censored weight biased samples. They discuss identifiability problems related to the underlying distribution and the distributions of the biased samples. They propose a test statistic based on the supremum of the weighted aggregated martingale residual processes from a number of such samples. Both numerical and graphical procedures are discussed, which the authors apply to do model checking for oil exploration drilling data.

引用

页码：475 / 490

页数：16

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