A smoothing expectation and substitution algorithm for the semiparametric accelerated failure time frailty model

2012 ◽  
Vol 31 (21) ◽  
pp. 2335-2358 ◽  
Author(s):  
Lynn M. Johnson ◽  
Robert L. Strawderman
Keyword(s):  
Failure Time ◽  
Frailty Model ◽  
Accelerated Failure Time ◽  
Accelerated Failure

scholarly journals Generalized Accelerated Failure Time Frailty Model for Systems Subject to Imperfect Preventive Maintenance

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Huilin Yin ◽  
Xiaohan Yang ◽  
Rui Peng
Keyword(s):  
Em Algorithm ◽  
Preventive Maintenance ◽  
Failure Time ◽  
Frailty Model ◽  
Remaining Useful Life ◽  
Model Parameters ◽  
Accelerated Failure Time ◽  
Ml Estimation ◽  
Industrial Systems ◽  
Accelerated Failure

Imperfect preventive maintenance (PM) activities are very common in industrial systems. For condition-based maintenance (CBM), it is necessary to model the failure likelihood of systems subject to imperfect PM activities. In this paper, the models in the field of survival analysis are introduced into CBM. Namely, the generalized accelerated failure time (AFT) frailty model is investigated to model the failure likelihood of industrial systems. Further, on the basis of the traditional maximum likelihood (ML) estimation and expectation maximization (EM) algorithm, the hybrid ML-EM algorithm is investigated for the estimation of parameters. The hybrid iterative estimation procedure is analyzed in detail. In the evaluation experiment, the generated data of a typical degradation model are verified to be appropriate for the real industrial processes with imperfect PM activities. The estimates of the model parameters are calculated using the training data. Then, the performance of the model is analyzed through the prediction of remaining useful life (RUL) using the testing data. Finally, comparison between the results of the proposed model and the existing model verifies the effectiveness of the generalized AFT frailty model.

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.

Sign in / Sign up

Export Citation Format

Share Document