A new generalized class of Kavya–Manoharan distributions: inferences and applications

This article introduces a new method of generating distributions by leveraging the concept of generalization with the hope of achieving more flexibility and greater adaptability. As a baseline distribution, we have considered a one-parameter exponential distribution. Along with studying the behavior of hazard rate, we have explored various statistical characteristics of the proposed distribution. For estimating model parameters we have employed the method of maximum likelihood estimation. To check the empirical validation of estimators obtained, the Monte Carlo simulation technique has been used. To show the model’s flexibility and competency, we have conducted a real data analysis using three real data sets and compared its performance with some widely used existing distributions.