The theory of competing risks has been developed to asses a specific risk in presence of other risk factors. In this paper we consider the parametric estimation of different failure modes under partially complete time and type of failure data using latent failure times and cause specific hazard functions models. Uniformly minimum variance unbiased estimators and maximum likelihood estimators are obtained when latent failure times and cause specific hazard functions are exponentially distributed. We also consider the case when they follow Weibull distributions. One data set is used to illustrate the proposed techniques
Abstract: In reliability or life-testing experiments, the cause of failure of an individual or item ...
We consider a nonparametric and a semiparametric (in presence of covariates) additive hazards rate c...
Competing risks are frequently overlooked, and the event of interest is analyzed with conventional s...
This paper is concerned with identification of a competing risks model with unknown transformations ...
In medical studies or in reliability analysis an investigator is often interested in the assessment ...
In this article, a competing risk model is analyzed in the presence of complete and censored data wh...
SUMMARY. This research develops methods which bring together models for multivari-ate failure time d...
In the competing risks model, a unit is exposed to several risks at the same time, but it is assumed...
Summary. In competing risks data, missing failure types (causes) is a very common phenomenon. In a g...
Competing risk or "multiple cause" survival data arise in medical, criminological, financial, engine...
AbstractIn competing risks model, several failure times arise potentially. The smallest failure time...
We present a Bayesian approach for analysis of competing risks survival data with masked causes of f...
We provide new conditions for identification of accelerated failure time competing risks models. The...
[[abstract]]This paper describes our studies on non-parametric maximum-likelihood estimators in a se...
In survival analysis or medical studies each person can be exposed to more than one type of outcomes...
Abstract: In reliability or life-testing experiments, the cause of failure of an individual or item ...
We consider a nonparametric and a semiparametric (in presence of covariates) additive hazards rate c...
Competing risks are frequently overlooked, and the event of interest is analyzed with conventional s...
This paper is concerned with identification of a competing risks model with unknown transformations ...
In medical studies or in reliability analysis an investigator is often interested in the assessment ...
In this article, a competing risk model is analyzed in the presence of complete and censored data wh...
SUMMARY. This research develops methods which bring together models for multivari-ate failure time d...
In the competing risks model, a unit is exposed to several risks at the same time, but it is assumed...
Summary. In competing risks data, missing failure types (causes) is a very common phenomenon. In a g...
Competing risk or "multiple cause" survival data arise in medical, criminological, financial, engine...
AbstractIn competing risks model, several failure times arise potentially. The smallest failure time...
We present a Bayesian approach for analysis of competing risks survival data with masked causes of f...
We provide new conditions for identification of accelerated failure time competing risks models. The...
[[abstract]]This paper describes our studies on non-parametric maximum-likelihood estimators in a se...
In survival analysis or medical studies each person can be exposed to more than one type of outcomes...
Abstract: In reliability or life-testing experiments, the cause of failure of an individual or item ...
We consider a nonparametric and a semiparametric (in presence of covariates) additive hazards rate c...
Competing risks are frequently overlooked, and the event of interest is analyzed with conventional s...