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

IF 0.7 4区 数学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Sort-Statistics and Operations Research Transactions Pub Date : 2017-06-21 DOI:10.2436/20.8080.02.54
E. Strzalkowska-Kominiak, A. Grané
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引用次数: 5

Abstract

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 ad- vantages over the widely used Pearson-type test. Finally, we apply our test to the head-and-neck cancer data.
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基于最大相关的随机删减数据的拟合优度检验
本文研究了随机截尾数据中基于最大相关系数的拟合优度检验方法。构造了一个新的广义右删减检验统计量,并证明了它的渐近性质。此外,我们还研究了一种特殊情况,即审查机制遵循著名的Koziol-Green模型。我们对这两个版本的检验统计量的经验能力进行了广泛的模拟研究,显示了它们比广泛使用的皮尔逊型检验的优势。最后,我们将我们的测试应用于头颈癌的数据。
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来源期刊
Sort-Statistics and Operations Research Transactions
Sort-Statistics and Operations Research Transactions 管理科学-统计学与概率论
CiteScore
3.10
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: SORT (Statistics and Operations Research Transactions) —formerly Qüestiió— is an international journal launched in 2003. It is published twice-yearly, in English, by the Statistical Institute of Catalonia (Idescat). The journal is co-edited by the Universitat Politècnica de Catalunya, Universitat de Barcelona, Universitat Autonòma de Barcelona, Universitat de Girona, Universitat Pompeu Fabra i Universitat de Lleida, with the co-operation of the Spanish Section of the International Biometric Society and the Catalan Statistical Society. SORT promotes the publication of original articles of a methodological or applied nature or motivated by an applied problem in statistics, operations research, official statistics or biometrics as well as book reviews. We encourage authors to include an example of a real data set in their manuscripts.
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