In this paper, generating extension forms for continuous probability distribution functions is studied. The considered transformer function is applied to three well-known probability distributions- Normal, Kumaraswamy, Weibull- and new extensions of these distributions are obtained. The related functions of the new extensions are defined, random samples are generated from the new extensions and the results are presented. Parameter estimation procedures of the extensions are studied, and likelihood equations are obtained. To demonstrate the modeling performance of the extensions, three different data sets are considered, separately. Each data set is modeled by both the corresponding probability distribution and its extensions. The new extensions give the best fit to the corresponding data over the well-known probability distributions.
Extensions of probability distributions Transformer function Quantile function Maximum likelihood estimation Skewness.
Primary Language | English |
---|---|
Subjects | Statistics |
Journal Section | Natural Sciences |
Authors | |
Publication Date | June 29, 2022 |
Submission Date | December 17, 2021 |
Acceptance Date | April 28, 2022 |
Published in Issue | Year 2022 |