Power-transformed linear regression on quantile residual life for censored competing risks data |
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Authors: | Caiyun Fan Yong Zhou |
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Institution: | 1. School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai, China;2. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China;3. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China;4. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China |
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Abstract: | ABSTRACTThis paper proposes a power-transformed linear quantile regression model for the residual lifetime of competing risks data. The proposed model can describe the association between any quantile of a time-to-event distribution among survivors beyond a specific time point and the covariates. Under covariate-dependent censoring, we develop an estimation procedure with two steps, including an unbiased monotone estimating equation for regression parameters and cumulative sum processes for the Box–Cox transformation parameter. The asymptotic properties of the estimators are also derived. We employ an efficient bootstrap method for the estimation of the variance–covariance matrix. The finite-sample performance of the proposed approaches are evaluated through simulation studies and a real example. |
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Keywords: | Box–Cox transformation Competing risks Empirical process Quantile residual life |
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