A Discrete Analog of the Inverted Kumaraswamy Distribution: Properties and Estimation with Application to COVID-19 Data.

In recent years, researchers focused on introducing discrete type distributions which satisfy the necessary demand to model the complex performance of the real data sets. In this paper, a discrete inverted Kumaraswamy distribution, which is a discrete version of the continuous inverted Kumaraswamy distribution, is derived using the general approach of discretization of a continuous distribution. The new discrete inverted Kumaraswamy distribution can be applied efficiently in discrete lifetime and count data. Some important distributional and reliability properties of discrete inverted Kumaraswamy distribution such as hazard rate, moments, quantiles, order statistics and some transformations are obtained. Maximum likelihood and Bayesian approaches are applied under Type-II censored samples for estimating the parameters, survival, hazard rate and alternative hazard rate functions. Confidence and credible intervals for the parameters are obtained. A simulation study is carried out to illustrate the theoretical results of the maximum likelihood and Bayesian estimation. Finally, the performance of the new distribution is compared with some distributions using three real data sets to illustrate the suitability and flexibility of the proposed model. [ABSTRACT FROM AUTHOR]