Bayesian and Non-Bayesian Estimation of Four-Parameter of Bivariate Discrete Inverse Weibull Distribution with Applications to Model Failure Times, Football, and Biological Data

Abstract

In this paper we have considered one model, namely the bivariate discrete inverse Weibull distribution, which has not been considered in the statistical literature yet. The proposed model is a discrete analogue of Marshall-Olkin inverse Weibull distribution. Some of its important statistical properties are studied. The proposed model has four parameters, and because of that it is a very flexible distribution. Maximum likelihood and Bayesian mmethods are used to estimate the model parameters. A detailed simulation study is carried out to examine the bias and mean square error of maximum likelihood and Bayesian estimators. Finally, three real data sets are analyzed to illustrate the importance of the proposed model.