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In this paper, we propose a new class of bivariate distributions, called the bivariate discrete inverse Weibull (BDsIW) distribution, whose marginals are discrete inverse Weibull (DsIW) distributions. Some statistical and mathematical properties are presented. The maximum likelihood method is used for estimating the model parameters. Simulations are presented to verify the performance of the direct maximum likelihood estimation. Finally, two real data sets are analyzed for illustrative purposes., Comment: arXiv admin note: text overlap with arXiv:1805.05199, arXiv:1701.03569 by other authors

In this article, the exponentiated discrete Lindley distribution is presented and studied. Some important distributional properties are discussed. Using the maximum likelihood method, estimation of the model parameters is investigated. Furthermore, simulation study is performed to observe the performance of the estimates. Finally, the model with two real data sets is examined.

The aim of this paper, is to define a bivariate exponentiated generalized linear exponential distribution based on Marshall-Olkin shock model. Statistical and reliability properties of this distribution are discussed. This includes quantiles, moments, stress-strength reliability, joint reliability function, joint reversed (hazard) rates functions and joint mean waiting time function. Moreover, the hazard rate, the availability and the mean residual lifetime functions for a parallel system, are established. One data set is analyzed, and it is observed that, the proposed distribution provides a better fit than Marshall-Olkin bivariate exponential, bivariate generalized exponential and bivariate generalized linear failure rate distributions. Simulation studies are presented to estimate both the relative absolute bias, and the relative mean square error for the distribution parameters based on complete data.

In this paper, we introduce a bivariate exponentaited generalized Weibull-Gompertz distribution. The model introduced here is of Marshall-Olkin type. Several properties are studied such as bivariate probability density function and it is marginal, moments, maximum likelihood estimation, joint reversed (hazard) function and joint mean waiting time and it is marginal. A real data set is analyzed and it is observed that the present distribution can provide a better fit than some other very well-known distributions.

This paper introduces studies on exponentaited generalized Weibull Gompertz distribution EGWGD which generalizes a lot of distributions. Several properties of the EGWGD such as reversed (hazard) function, moments, maximum likelihood estimation, mean residual (past) lifetime, MTTF, MTTR, MTBF, maintainability, availability and order statistics are studied in this paper. A real data set is analyzed and it is observed that the present distribution can provide a better fit than some other very well known distributions, Comment: 16 pages,5 figures