Smoothed Jackknife Empirical Likelihood for Weighted Rank Regression with Censored Data

Longlong Huang, Karen Kopciuk and Xuewen Lu
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Longlong Huang: Department of Mathematics and Statistics, University of Calgary, Canada
Karen Kopciuk: Department of Mathematics and Statistics, University of Calgary, Canada
Xuewen Lu: Department of Mathematics and Statistics, University of Calgary, Canada

Biostatistics and Biometrics Open Access Journal, 2018, vol. 6, issue 2, 48-67

Abstract: To make inference for the semiparametric accelerated failure time (AFT) model with right censored data, which may contain outlying response or covariate values, we propose a smoothed jackknife empirical likelihood (JEL) method for the U -statistic obtained from a weighted smoothed rank estimating function. The jackknife empirical likelihood ratio is shown to be a standard chi-squared statistic. The new method improves upon the inference of the normal approximation method and possesses desirable important properties of easy computation and double robustness against influence of both outlying response and covariates. The advantages of the new method are demonstrated by simulation studies and data analyses.

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:6:y:2018:i:2:p:48-67

DOI: 10.19080/BBOAJ.2018.06.555685

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