In parametric statistical modeling, it is important to construct new extensions of existing
probability distributions (PDs) that can make modeling data more flexible and help …

Modeling and predicting time-to-event phenomena in engineering, sports, and medical
sectors are very crucial. Numerous models have been proposed for modeling such types of …

In this work, we propose and study a novel generalization of the Fréchet distribution called
the odd beta prime Fréchet (OBPF) distribution. This distribution was an extension of the …

To define useful numbers having values from 0 to 1, as percentages, probabilities, and
proportions, the unit probability models are often employed in probability and statistics. In …

In the field of biomedical research, data characteristics often exhibit significant variability,
challenging the applicability of classical probability distributions for biomedical data …

Probability distributions are very important in describing practical phenomena. Among the
available probability distributions, the three-parameter generalized inverse Weibull (for short …

Since standard distributions do not fundamentally have an acceptable fit to all types of data
sets, it is necessary to construct extensions of standard distributions to increase their …

In the literature, modifying the baseline distributions and proposing novel approach of the
existing probability models play an important role in the analysis of real-world phenomena …

The present paper discusses a versatile three-parameter distribution from the Yun-G family.
The important statistical properties like moments, stochastic ordering, and entropy are …

This paper investigates the E-Bayesian and hierarchical Bayesian estimations of shape
parameter and reliability function of Kumaraswamy generalized distribution based on upper …