scholarly journals Nonparametric identification of accelerated failure time competing risks models

2010 ◽  
Author(s):  
Arthur Lewbel ◽  
Sokbae (Simon) Lee
Keyword(s):  
Competing Risks ◽  
Failure Time ◽  
Nonparametric Identification ◽  
Accelerated Failure Time ◽  
Accelerated Failure

scholarly journals NONPARAMETRIC IDENTIFICATION OF ACCELERATED FAILURE TIME COMPETING RISKS MODELS

2013 ◽  
Vol 29 (5) ◽  
pp. 905-919 ◽  
Author(s):  
Sokbae Lee ◽  
Arthur Lewbel
Keyword(s):  
Competing Risks ◽  
Failure Time ◽  
Nonparametric Identification ◽  
Accelerated Failure Time ◽  
Rank Condition ◽  
Survivor Function ◽  
Auction Models ◽  
Regression Functions ◽  
Accelerated Failure ◽  
Identification Strategy

We provide new conditions for identification of accelerated failure time competing risks models. These include Roy models and some auction models. In our setup, unknown regression functions and the joint survivor function of latent disturbance terms are all nonparametric. We show that this model is identified given covariates that are independent of latent errors, provided that a certain rank condition is satisfied. We present a simple example in which our rank condition for identification is verified. Our identification strategy does not depend on identification at infinity or near zero, and it does not require exclusion assumptions. Given our identification, we show estimation can be accomplished using sieves.

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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