Goodness-of-fit test for randomly censored data based on maximum correlation

被引：6

作者：

Strzalkowska-Kominiak, Ewa ^{[1
]}

Grane, Aurea ^{[2
]}

机构：

[1] Cracow Univ Technol, Math Inst, Warszawska 24, PL-31155 Krakow, Poland

[2] Univ Carlos III Madrid, Stat Dept, C Madrid 126, E-28903 Getafe, Spain

来源：

SORT-STATISTICS AND OPERATIONS RESEARCH TRANSACTIONS | 2017年 / 41卷 / 01期

关键词：

Goodness-of-fit; Kaplan-Meier estimator; maximum correlation; random censoring; KAPLAN-MEIER ESTIMATOR; ASYMPTOTIC PROPERTIES;

D O I：

10.2436/20.8080.02.54

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

In this paper we study a goodness-of-fit test based on the maximum correlation coefficient, in the context of randomly censored data. We construct a new test statistic under general right censoring and prove its asymptotic properties. Additionally, we study a special case, when the censoring mechanism follows the well-known Koziol-Green model. We present an extensive simulation study on the empirical power of these two versions of the test statistic, showing their advantages over the widely used Pearson-type test. Finally, we apply our test to the head-and-neck cancer data.

引用

页码：119 / 138

页数：20

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