A Novel Sin Model SMEx with Application on COVID-19 and Precipitation Data

Abstract

The present study proposes a new and flexible trigonometric extension of the moment exponential distribution, termed the Sine Moment Exponential (SMEx) distribution, developed using the sine-G family of distributions. This model offers an attractive alternative to well-known lifetime distributions by providing enhanced flexibility for analyzing lifetime datasets that exhibit leptokurtic or platykurtic behavior. Several statistical properties of the SMEx distribution are derived, including its moments, quantile function, mean residual life, and order statistics. To assess its performance, five different estimation approaches are applied, including Anderson-Darling estimation, maximum likelihood estimation, Cramervon Mises estimation, ordinary least squares estimation, and weighted least squares estimation. A detailed Monte Carlo simulation study is utilized to illustrate the estimation behavior of these considered estimation procedures. In the end, two datasets associated with COVID-19 and precipitation are utilized to illustrate the applicability and flexibility of the proposed distribution. It is found that the proposed distribution efficiently analyzed these datasets as compared to competitive distributions.OPEN ACCESS Received: 06/10/2025 Accepted: 28/10/2025 Published: 23/01/2026

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