Semiparametric maximum likelihood estimation in Cox proportional hazards model with covariate measurement errors

Metrika ◽  
2009 ◽  
Vol 72 (2) ◽  
pp. 199-217 ◽  
Author(s):  
Chi-Chung Wen
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Measurement Errors ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Likelihood Estimation ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Hazards Model ◽  
Covariate Measurement Errors

scholarly journals Testing Linear and Nonlinear Hypotheses in a Cox Proportional Hazards Model with Errors in Covariates

2019 ◽  
Vol 58 (1) ◽  
pp. 39-47
Author(s):  
Oksana Chernova ◽  
Alexander Kukush
Keyword(s):  
Measurement Errors ◽  
Hazard Rate ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Baseline Hazard ◽  
Hazards Model ◽  
Regression Parameters ◽  
Linear And Nonlinear

We investigate linear and nonlinear hypotheses testing in a Cox proportional hazards model for right-censored survival data when the covariates are subject to measurement errors. In Kukush and Chernova (2018) [Theor. Probability and Math. Statist. 96, 101–110], a consistent simultaneous estimator is introduced for the baseline hazard rate and the vector of regression parameters. Therein the baseline hazard rate belongs to an unbounded set of nonnegative Lipschitz functions, with fixed constant, and the vector of regression parameters belongs to a compact parameter set. Based on the estimator, we develop two procedures to test nonlinear and linear hypotheses about the vector of regression parameters: Wald-type and score-type tests. The latter is based on an unbiased estimating equation. The consistency of the tests is shown.

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