In this paper, we introduced the discrete analogue of the continuous Marshall-Olkin Weibull distribution using the discrete concentration approach. Some mathematical properties of the proposed discrete distribution such as the probability mass function, cumulative distribution function, survival function, hazard rate function, second rate of failure, probability generating function, quantile function and moments are derived. The method of maximum likelihood estimation is employed to estimate the unknown parameters of the proposed distribution. The applicability of the proposed discrete distribution was examined using an over-dispersed and under-dispersed data sets.

This paper presents a new generalized bounded distribution called the Kumaraswamy unit-Gompertz (KUG) distribution. Some of the Mathematical properties which include; the density function, cumulative distribution function, survival and hazard rate functions, quantile, mode, median, moment, moment generating function, Renyi entropy and distribution of order statistics are derived. We employ the maximum likelihood estimation method to estimate the unknown parameters of the proposed KUG distribution. A Monte Carlo simulation study is carried out to investigate the performance of the maximum likelihood estimates of the unknown parameters of the proposed distribution. Two real datasets are used to illustrate the applicability of the proposed KUG distribution in lifetime data analysis.

In this paper, a new generalized distribution known as Weibull Logistic-Exponential Distribution (WLED) is proposed using the T-R{Y} framework. Several mathematical properties of this new distribution are studied. The maximum likelihood estimation method was used in estimating the parameters of the proposed distribution. Finally, an application of the proposed distribution to a real lifetime data set is presented and its fit was compared with the fit obtained by some comparable lifetime distributions.