Unit Shiha Distribution and its Applications to Engineering and Medical Data

TL;DR

This paper introduces the unit Shiha (USh) distribution, a flexible new statistical model for data on the unit interval, capable of capturing various skewness and failure-rate patterns, with demonstrated superior fit on real data.

Contribution

The paper proposes the novel unit Shiha distribution derived from the Shiha distribution via inverse exponential transformation, expanding modeling options for bounded data.

Findings

USh distribution effectively models skewed data.

The distribution captures diverse hazard rate shapes.

It outperforms existing models in real data applications.

Abstract

There is a growing need for flexible statistical distributions that can accurately model data defined on the unit interval. This paper introduces a new unit distribution, termed the unit Shiha (USh) distribution, which is derived from the original Shiha (Sh) distribution through an inverse exponential transformation. The probability density function of the USh distribution is sufficiently flexible to model both left- and right-skewed data, while its hazard rate function is capable of capturing various failure-rate patterns, including increasing, bathtub-shaped, and J-shaped forms. Several statistical properties of the proposed distribution are investigated, including moments and related measures, the quantile function, entropy, and stress-strength reliability. Parameter estimation is carried out using the maximum likelihood method, and its performance is evaluated through a simulation study. The practical usefulness of the USh distribution is demonstrated using four real-life data sets, and its performance is compared with several well-known competing unit distributions. The comparative results indicate that the proposed model fits the data better than the competitive models applied in this study.