Compound negative binomial shared frailty models for bivariate survival data

Hanagal, David D.; Dabade, Alok D.

doi:10.1016/j.spl.2013.07.005

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…The most important properties of the proposed models that were not mentioned in the previous study are the estimates of the frailty variances are high in all proposed models as compared to previous study given by McGilchrist and Aisbett [18] on log-normal frailty, Hanagal and Bhambure [19], the disease type GN and AN has lower infection rates as compared to other covariates. All the covariates are significant factors for kidney infection, but the disease type are insignificant in the previous proposed frailty models (see [4]). It is very crucial to be mention that Lindly shared frailty model based on generalized Rayleigh baseline distribution is performed better to analyze kidney infection data than other frailty models [4,19].…”

Section: Discussionmentioning

confidence: 92%

“…All the covariates are significant factors for kidney infection, but the disease type are insignificant in the previous proposed frailty models (see [4]). It is very crucial to be mention that Lindly shared frailty model based on generalized Rayleigh baseline distribution is performed better to analyze kidney infection data than other frailty models [4,19].…”

Section: Discussionmentioning

confidence: 92%

“…Generally, gamma distribution, log-normal distribution and inverse Gaussian distributions are the most commonly used frailty distributions [2,3]. Hanagal and Dabade [4] introduced new Compound negative binomial shared frailty models for bivariate survival data using Weibull and generalized exponential as baseline distributions. Pandey et al [5] compared gamma, inverse Gaussian and positive frailty models with generalized Pareto as baseline distribution.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Heterogeneity Using Lindley Distribution

Pandey¹,

Lalpawimawha²

2022

Computational Statistics and Applications

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Frailty models are intended for use in survival analysis to explain unobserved heterogeneity in an individual caused by various hereditary variables or environmental influences. A shared frailty model was utilized to examine the data. It is based on the idea that frailty affects the hazard rate in a multiplicative manner. In this manuscript, we introduce a new frailty model called the Lindley shared frailty model with exponential power and generalized Rayleigh as baseline distributions. The The Bayesian method of the Monte Carlo method of the Markov chain is used to estimate the parameters used in the model; simulation studies are also carried out to compare the actual and calculated values of the parameters; the proposed model is compared with the Bayesian comparison method Compare and propose the best model of infectious disease data.

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Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Modeling Heterogeneity Using Lindley Distribution

Pandey¹,

Lalpawimawha²

2022

Computational Statistics and Applications

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“…Gerber (1984), dhaene (1991), Vellaisamy & Upadhye (2009a) and Upadhye & Vellaisamy (2014) considered the problem of approximating a compound negative binomial distribution by a compound Poisson distribution. Hanagal & Dabade (2013) introduced compound negative binomial frailty model with three baseline distributions.…”

Section: Introductionmentioning

confidence: 99%

A Bivariate Model based on Compound Negative Binomial Distribution

Omair¹,

Almuhayfith²,

Alzaid³

2018

Rev. colomb. estad.

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A new bivariate model is introduced by compounding negative binomial and geometric distributions. Distributional properties, including joint, marginal and conditional distributions are discussed. Expressions for the product moments, covariance and correlation coecient are obtained. Some properties such as ordering, unimodality, monotonicity and self-decomposability are studied. Parameter estimators using the method of moments and maximum likelihood are derived. Applications to trac accidents data are illustrated.

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Various Frailty Models

Hanagal

2019

Modeling Survival Data Using Frailty Models

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Compound negative binomial shared frailty models for bivariate survival data

Cited by 7 publications

References 12 publications

Modeling Heterogeneity Using Lindley Distribution

Modeling Heterogeneity Using Lindley Distribution

A Bivariate Model based on Compound Negative Binomial Distribution

Various Frailty Models

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