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2022 | 23 | nr 4 | 161--176
Tytuł artykułu

Generalised Lindley Shared Additive Frailty Regression Model for Bivariate Survival Data

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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. (original abstract)
Rocznik
Tom
23
Numer
Strony
161--176
Opis fizyczny
Twórcy
  • Central University of Rajasthan, India
  • Savitribai Phule Pune University, India
  • Central University of Rajasthan, India
Bibliografia
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  • Gupta, P., Pandey, A., and Tyagi, S., (2022). Comparison of Multiplicative Frailty Models under Generalized Log-Logistic-II Baseline Distribution for Counter Heterogeneous Left Censored Data, 1, pp. 97-114.
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  • Pandey, A., Bhushan, S., Pawimawha, L., and Tyagi, S., (2020a).Analysis of Bivariate Survival Data using Shared Inverse Gaussian Frailty Models: A Bayesian Approach, Predictive Analytics Using Statistics and Big Data: Concepts and Modeling, Bentham Books, 14, pp. 75-88.
  • Pandey, A., Hanagal, D. D., Gupta, P., & Tyagi, S., (2020b). Analysis of Australian Twin Data Using Generalized Inverse Gaussian Shared Frailty Models Based on Reversed Hazard Rate. International Journal of Statistics and Reliability Engineering, 7(2), pp. 219-235.
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  • Tyagi, S., Pandey, A. & Chesneau, C.,(2022b). Weighted Lindley Shared Regression Model for Bivariate Left Censored Data. Sankhya B., https://doi.org/10.1007/s13571-022- 00278-1.
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171662968

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