scholarly journals Analysis of accelerated failure time data with dependent censoring using auxiliary variables via nonparametric multiple imputation

2015 ◽  
Vol 34 (19) ◽  
pp. 2768-2780 ◽  
Author(s):  
Chiu-Hsieh Hsu ◽  
Jeremy M. G. Taylor ◽  
Chengcheng Hu
Keyword(s):  
Multiple Imputation ◽  
Failure Time ◽  
Dependent Censoring ◽  
Accelerated Failure Time ◽  
Failure Time Data ◽  
Auxiliary Variables ◽  
Time Data ◽  
Accelerated Failure

Accelerated failure time models with log-concave errors

2019 ◽  
Vol 23 (2) ◽  
pp. 251-268
Author(s):  
Ruixuan Liu ◽  
Zhengfei Yu
Keyword(s):  
Maximum Likelihood ◽  
Failure Time ◽  
Maximum Likelihood Estimates ◽  
Accelerated Failure Time ◽  
Time Data ◽  
Dimensional Parameter ◽  
Nonparametric Maximum Likelihood Estimator ◽  
Finite Dimensional ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure

Summary We study accelerated failure time models in which the survivor function of the additive error term is log-concave. The log-concavity assumption covers large families of commonly used distributions and also represents the aging or wear-out phenomenon of the baseline duration. For right-censored failure time data, we construct semiparametric maximum likelihood estimates of the finite-dimensional parameter and establish the large sample properties. The shape restriction is incorporated via a nonparametric maximum likelihood estimator of the hazard function. Our approach guarantees the uniqueness of a global solution for the estimating equations and delivers semiparametric efficient estimates. Simulation studies and empirical applications demonstrate the usefulness of our method.

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