Results 1 
4 of
4
An EM Algorithm for Estimating the Parameters of Bivariate Weibull Distribution under Random
 Censoring, Computational Statistics & Data Analysis
, 2010
"... distribution under random censoring ..."
Weibull Extension of Bivariate Exponential Regression Model with Gamma Frailty for Survival Data
"... Abstract: A maximum likelihood estimation procedure is developed for a bivariate frailty regression model, in which dependence is generated by a gamma distribution. It is assumed that the random lifetimes follow a bivariate Weibull distribution proposed in Hanagal [7] and that censoring is independe ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
Abstract: A maximum likelihood estimation procedure is developed for a bivariate frailty regression model, in which dependence is generated by a gamma distribution. It is assumed that the random lifetimes follow a bivariate Weibull distribution proposed in Hanagal [7] and that censoring is independent of the two lifetimes. The proposed model may be applied for survival times in genetic epidemiology, survival times of dental implants of patients and survival times of twin births (both monozygotic and dizygotic), with genetic frailty behavior (which is unknown and random) of patients.
MODELING HETEROGENEITY FOR BIVARIATE SURVIVAL DATA BY POWER VARIANCE FUNCTION DISTRIBUTION
"... We propose a bivariate Weibull regression model with frailty which is generated by power variance function distribution. We assume that the bivariate survival data follow bivariate Weibull of Hanagal (2005a) and distribution of censoring variable is independent of the two life times. There are some ..."
Abstract
 Add to MetaCart
(Show Context)
We propose a bivariate Weibull regression model with frailty which is generated by power variance function distribution. We assume that the bivariate survival data follow bivariate Weibull of Hanagal (2005a) and distribution of censoring variable is independent of the two life times. There are some interesting situations like survival times in genetic epidemiology, survival times of dental implants of patients and survival times of twin births (both monozygotic and dizygotic) where genetic behavior (which is unknown and random) of patients follows a power variance function frailty distribution. These are the situations which motivate to study this particular model. We propose two stage maximum likelihood estimation procedure for the parameters and develop large sample tests for no frailty and the significance of regression parameters in the proposed model.
Estimating the Parameters of the Marshall Olkin Bivariate Weibull Distribution by EM Algorithm
"... In this paper we consider the MarshallOlkin bivariate Weibull distribution. The MarshallOlkin bivariate Weibull distribution is a singular distribution, whose both the marginals are univariate Weibull distributions. This is a generalization of the MarshallOlkin bivariate exponential distribution. ..."
Abstract
 Add to MetaCart
(Show Context)
In this paper we consider the MarshallOlkin bivariate Weibull distribution. The MarshallOlkin bivariate Weibull distribution is a singular distribution, whose both the marginals are univariate Weibull distributions. This is a generalization of the MarshallOlkin bivariate exponential distribution. The cumulative joint distribution of the MarshallOlkin bivariate Weibull distribution is a mixture of an absolute continuous distribution function and a singular distribution function. This distribution has four unknown parameters and it is observed that the maximum likelihood estimators of the unknown parameters can not be obtained in explicit forms. In this paper we discuss about the computation of the maximum likelihood estimators of the unknown parameters using EM algorithm. We perform some simulations to see the performances of the EM algorithm and reanalyze one data set for illustrative purpose.