Bayesian and Non-Bayesian Analysis for the Sine Generalized Linear Exponential Model under Progressively Censored Data

Abstract

This article introduces a novel variant of the generalized linear exponential (GLE) distribution, known as the sine generalized linear exponential (SGLE) distribution. The SGLE distribution utilizes the sine transformation to enhance its capabilities. The updated distribution is very adaptable and may be efficiently used in the modeling of survival data and dependability issues. The suggested model incorporates a hazard rate function (HRF) that may display a rising, J-shaped, or bathtub form, depending on its unique characteristics. This model includes many well-known lifespan distributions as separate sub-models. The suggested model is accompanied with a range of statistical features. The model parameters are examined using the techniques of maximum likelihood and Bayesian estimation using progressively censored data. In order to evaluate the effectiveness of these techniques, we provide a set of simulated data for testing purposes. The relevance of the newly presented model is shown via two real-world dataset applications, highlighting its superiority over other respected similar models.