Power Pratibha Distribution with Properties and Applications
Abstract
In this paper, power Pratibha distribution using the power transformation on Pratibha distribution has been proposed and its significant statistical and mathematical properties, such as moments and related measures, survival function, hazard function, reverse hazard function, mean residual life function, and stochastic ordering, have been studied. Maximum likelihood estimation is used to estimate the parameters of the distribution. In order to determine whether the maximum likelihood estimate of the parameters remains consistent as the sample size increases, a simulation study is conducted. Finally, two examples of real lifetime datasets from engineering and banking sector have been presented to demonstrate its applications and the goodness of fit shows that the proposed distribution provides a better fit over two-parameter Weibull distribution, power Lindley distribution, power Sujatha distribution, power Aradhana distribution, power Akash distribution, power Komal distribution, power Uma distribution and one parameter Sujatha distribution and Pratibha distribution.
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