Statistical inference and applications of Kumaraswamy Odd Perks power Lomax distribution

In this paper, we introduce the novel Kumaraswamy Odd Perks power Lomax distribution, which provides flexibility in modelling data that exhibit wide range of density shapes such as left-skewed, right-skewed, decreasing, and decreasing-increasing-decreasing, and hazard behaviors like sigmoid, inverted V, monotonically increasing, monotonically decreasing and j-shaped curve. We derive closed-form expressions for the quantile function, survival and hazard functions, and present a generalized linear representation of the distribution and density functions of the proposed Kumaraswamy Odd Perks power Lomax distribution. The paper also presents the moments, moment generating function, order statistics and entropy. The estimation of parameters is performed using multiple techniques like maximum likelihood estimation, maximum product spacing, least squares, weighted least squares, Cramér-von Mises, Anderson-Darling, and right-tailed Anderson–Darling estimation. The performance of the estimation methods was assessed using Monte Carlo simulations. The potential of the proposed model is illustrated by its applicability in modelling real-world datasets. The proposed model outperforms other competing models in terms of goodness-of-fit and adequacy measures.