Modeling heterogeneity for bivariate survival data by the compound Poisson distribution with random scale
We propose a bivariate Weibull regression model with heterogeneity (frailty or random effect) which is generated by compound Poisson distribution with random scale. We assume that the bivariate survival data follow bivariate Weibull of Hanagal (2004). There are some interesting situations like survival times in genetic epidemiology, dental implants of patients and twin births (both monozygotic and dizygotic) where genetic behavior (which is unknown and random) of patients follows a known frailty distribution. These are the situations which motivate us to study this particular model. We propose a two stage maximum likelihood estimation procedure for the parameters in the proposed model and develop large sample tests for testing significance of regression parameters.
Year of publication: |
2010
|
---|---|
Authors: | Hanagal, David D. |
Published in: |
Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 80.2010, 23-24, p. 1781-1790
|
Publisher: |
Elsevier |
Keywords: | Bivariate Weibull Compound Poisson Frailty Parametric regression Survival times |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
Gamma shared frailty model based on reversed hazard rate for bivariate survival data
Hanagal, David D., (2014)
-
Compound negative binomial shared frailty models for bivariate survival data
Hanagal, David D., (2013)
-
Hanagal, David D., (2012)
- More ...