Classical and Bayesian estimation of the induced Chriss-Jerry distribution with applications to radiation and waiting bank times data

This paper proposes a novel one-parameter model, termed the induced Chris-Jerry distribution, utilizing the induced transformation method applied to the Chris-Jerry distribution to achieve flexible distributions. Our research analyzed various essential features and characteristics, including moments, moment generating function, order statistics, quantile functions, and Rényi entropy. The parameter of the proposed model is estimated using maximum likelihood, maximum product of spacings, and Bayesian estimation methods. Monte Carlo simulation research is performed utilizing an efficient approach to evaluate the behavior of various estimations. The induced Chris-Jerry distribution demonstrates a strong fit, making it a viable option when compared to certain competing models based on defined criteria for the datasets.