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Gamma Frailty Regression Models in Mixture Distributions
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David D. Hanagal
Published/Copyright:
March 11, 2010
Abstract
We propose frailty regression models in mixture distributions assuming the distribution of frailty to be a gamma distribution. We consider Weibull mixture as an example. 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 to study this particular model.
Key words:: Gamma Frailty; Gamma mixture frailty; Mixture distribution; Parametric regression; Survival times; Weibull model
Published Online: 2010-03-11
Published in Print: 2007-October
© Heldermann Verlag
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Keywords for this article
Gamma Frailty;
Gamma mixture frailty;
Mixture distribution;
Parametric regression;
Survival times;
Weibull model
Articles in the same Issue
- Estimation on a GAR(1) Process by the EM Algorithm
- Point and Interval Estimation for the Lifetime Distribution of a k-Unit Parallel System Based on Progressively Type-II Censored Data
- A Note on the Distribution of Bousquet
- A Quality Index for Evaluating the Bank Capital Adequacy According to Basel I and II
- Reliability Test Plans for Series Systems in the Presence of Covariates
- Multivariate Information in Univariate Control Charts
- A Note on the Moments of Random Variables
- A Heuristic Approach for Constrained Redundancy Optimization in Multi-state Systems
- Reliability Computation of Moranda's Geometric Software Reliability Model
- Economic Design of A Modified Variable Sample Size and Sampling Interval Chart
- Gamma Frailty Regression Models in Mixture Distributions
- A Truncated Bivariate t Distribution
