Abstract

In this research paper, we make a significant attempt to present a novel statistical methodological approach for generating more versatile distributions. The proposed method is developed by incorporating the Logarithmic and Sine functions and may be referred to as a novel logarithmic sine-G (NLS-G) family of distributions. A special sub-model of the NLSG family is investigated by utilizing the Weibull model as a base member. The sub-model of the NLS-G family may be callednovel logarithmic sineWeibull (NLS-Weibull) distribution. The density and hazard functions of the NLS-Weibull distribution are graphically illustrated, showing their behavior and characteristics. Some distributional properties of the NLSG family, including quantile function, median and quartile measures, moments, and moment generating function, are derived. The Maximum Likelihood Estimation (MLE) method is employed for estimating the model parameters of the NLS-G family of distributions. A comprehensive Monte Carlo simulation study of the proposed distribution is also conducted to evaluate the practical performance of its estimators. Furthermore, the usefulness of the newly proposed NLS-Weibull distribution is illustrated by investigating four real data sets from the field of the engineering sector. The first data set represents the failure time of electronic devices. The second data set is civil engineering data and represents the breaking stress of carbon fibers. The third data set represents the strengths of 1.5 cm glass fibers. The fourth data set represents single-carbon fibers. Based on four diagnostic criteria, it is observed that the NLS-Weibull distribution may be the best choice for the considered data sets.OPEN ACCESS Received: 02/12/2025 Accepted: 12/01/2026 Published: 29/05/2026