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  • 标题:Goodness-of-fit test for randomly censored data based on maximum correlation
  • 本地全文:下载
  • 作者:Ewa Strzalkowska-Kominiak ; Aurea Grané
  • 期刊名称:SORT-Statistics and Operations Research Transactions
  • 印刷版ISSN:2013-8830
  • 出版年度:2017
  • 卷号:1
  • 期号:1
  • 页码:119-138
  • 语种:English
  • 出版社:SORT- Statistics and Operations Research Transactions
  • 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 ad- vantages over the widely used Pearson-type test. Finally, we apply our test to the head-and-neck cancer data.
  • 其他摘要: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 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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