Kumaraswamy Generalized Half-Logistic Distribution

Abstract

Statistical distributions play an essential part in the process of interpreting experimental data; nevertheless, choosing a distribution that is appropriate for the data that is currently available is not an easy task.Extending a known family of distribution to construct a new one is a long-honored technique.We suggest a new distribution named kumaraswamy generalized half-Logistic distribution (KW-GHLD).This distribution is obtained by adding two parameters to the existing model to raise its ability to fit complex data sets.Many mathematical and statistical properties were investigated, such as the survival function, the hazard function, the moment, the moment generating function, the incomplete moments, the Renyi entropy, the stochastic ordering, the probabilityweighted moments, the order statistics, and the quantile function.The maximum likelihood method is utilized to make estimates for the KW-GHL distribution's unknown parameters.We study the efficacy of the desired distribution (KW-GHLD) by applying it to some real data set, which has been discussed within the measures of goodness of fit (AIC, BIC, CAIC, and QHIC) and comparing the outcomes with those obtained by the original distribution (GHLD), which produced best outcomes.This allowed us to determine whether or not the desired distribution is effective.Finally, we present several conclusions related to our findings.