Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/301895 
Year of Publication: 
2022
Citation: 
[Journal:] Statistics in Transition new series (SiTns) [ISSN:] 2450-0291 [Volume:] 23 [Issue:] 4 [Year:] 2022 [Pages:] 161-176
Publisher: 
Sciendo, Warsaw
Abstract: 
Frailty models are the possible choice to counter the problem of the unobserved heterogeneity in individual risks of disease and death. Based on earlier studies, shared frailty models can be utilised in the analysis of bivariate data related to survival times (e.g. matched pairs experiments, twin or family data). In this article, we assume that frailty acts additively to the hazard rate. A new class of shared frailty models based on generalised Lindley distribution is established. By assuming generalised Weibull and generalised log-logistic baseline distributions, we propose a new class of shared frailty models based on the additive hazard rate. We estimate the parameters in these frailty models and use the Bayesian paradigm of the Markov Chain Monte Carlo (MCMC) technique. Model selection criteria have been applied for the comparison of models. We analyse kidney infection data and suggest the best model.
Subjects: 
Bayesian estimation
frailty
generalised Lindley frailty
generalised log-logistic distribution
generalised Weibull distribution
hazard rate
MCMC
random censoring
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-sa Logo
Document Type: 
Article

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