Comparison of variance estimation methods in semiparametric accelerated failure time models for multivariate failure time data

2021 ◽  
Author(s):  
Kyuhyun Kim ◽  
Jungyeol Ko ◽  
Sangwook Kang
Keyword(s):  
Variance Estimation ◽  
Failure Time ◽  
Estimation Methods ◽  
Accelerated Failure Time ◽  
Failure Time Data ◽  
Time Data ◽  
Multivariate Failure Time ◽  
Accelerated Failure Time Models ◽  
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.

Effects of covariates on alternating recurrent events in accelerated failure time models

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Moumita Chatterjee ◽  
Sugata Sen Roy
Keyword(s):  
Recurrent Events ◽  
Failure Time ◽  
Likelihood Method ◽  
Model Parameters ◽  
Accelerated Failure Time ◽  
Survival Times ◽  
Copula Functions ◽  
Event Time ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure

AbstractIn this article, we model alternately occurring recurrent events and study the effects of covariates on each of the survival times. This is done through the accelerated failure time models, where we use lagged event times to capture the dependence over both the cycles and the two events. However, since the errors of the two regression models are likely to be correlated, we assume a bivariate error distribution. Since most event time distributions do not readily extend to bivariate forms, we take recourse to copula functions to build up the bivariate distributions from the marginals. The model parameters are then estimated using the maximum likelihood method and the properties of the estimators studied. A data on respiratory disease is used to illustrate the technique. A simulation study is also conducted to check for consistency.

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