Statistical inference for dependent competing risks data under adaptive Type-II progressive hybrid censoring

In this article, we consider statistical inference based on dependent competing risks data from Marshall-Olkin bivariate Weibull distribution. The maximum likelihood estimates of the unknown model parameters have been computed by using the Newton-Raphson method under adaptive Type II progressive hybrid censoring with partially observed failure causes. The existence and uniqueness of maximum likelihood estimates are derived. Approximate confidence intervals have been constructed via the observed Fisher information matrix using the asymptotic normality property of the maximum likelihood estimates. Bayes estimates and highest posterior density credible intervals have been calculated under gamma-Dirichlet prior distribution by using the Markov chain Monte Carlo technique. Convergence of Markov chain Monte Carlo samples is tested. In addition, a Monte Carlo simulation is carried out to compare the effectiveness of the proposed methods. Further, three different optimality criteria have been taken into account to obtain the most effective censoring plans. Finally, a real-life data set has been analyzed to illustrate the operability and applicability of the proposed methods

Generalized Marshall-Olkin Distributions, and Related Bivariate Aging Properties

National Natural Science Foundation of China [10771090]A class of generalized bivariate Marshall-Olkin distributions, which includes as special cases the Marshall-Olkin bivariate exponential distribution and the Marshall-Olkin type distribution due to Muliere and Scarsini (1987) [19] are examined in this paper. Stochastic comparison results are derived, and bivariate aging properties, together with properties related to evolution of dependence along time, are investigated for this class of distributions. Extensions of results previously presented in the literature are provided as well. (C) 2011 Elsevier Inc. All rights reserved

Semi-Parametric Likelihood Functions for Bivariate Survival Data

Because of the numerous applications, characterization of multivariate survival distributions is still a growing area of research. The aim of this thesis is to investigate a joint probability distribution that can be derived for modeling nonnegative related random variables. We restrict the marginals to a specified lifetime distribution, while proposing a linear relationship between them with an unknown (error) random variable that we completely characterize. The distributions are all of positive supports, but one class has a positive probability of simultaneous occurrence. In that sense, we capture the absolutely continuous case, and the Marshall-Olkin type with a positive probability of simultaneous event on a set of measure zero. In particular, the form of the joint distribution when the marginals are of gamma distributions are provided, combining in a simple parametric form the dependence between the two random variables and a nonparametric likelihood function for the unknown random variable. Associated properties are studied and investigated and applications with simulated and real data are given

On a New Class of Bivariate Survival Distributions Based on the Model of Dependent Lives and its Generalization

In this paper, a new class of survival distributions based on the model of dependent lives and proportional hazard rate family is introduced. This new family of bivariate survival models contains several bivariate lifetime models and is more flexible. The main purpose of this paper is to generalize this family of bivariate survival distributions of dependent lives so that more flexible models can be achieved. These new families of distributions are called the bivariate proportional hazard rate (BPHR) and the bivariate proportional hazard rate-geometric (BPHRG) families, respectively. It is also observed that, if θ = 1, then the BPHR family is a particular state of the BPHRG family. Several features of these new families of distributions such as the multivariate aging properties, the bivariate hazard gradient, and dependency structures are investigated. We design a flexible computational EM algorithm to calculate the maximum likelihood estimation of parameters. Also, several simulation studies are represented to evaluate the efficiency of the EM algorithm. Finally, we analyze three real datasets and compare the BPHRG models with the BPHR models