Rao-Robson-Nikulin Goodness-of-fit Test Statistic for Censored and Uncensored Real Data with Classical and Bayesian Estimation

Abstract

In this work, we provide a new Pareto type-II extension for censored and uncensored real-life data. With an emphasis on the applied elements of the model, some mathematical properties of the new distribution are deduced without excess. A variety of traditional methods, including the Bayes method, are used to estimate the parameters of the new distribution. The censored case maximum likelihood technique is also inferred. Using Pitman's proximity criteria, the likelihood estimation and the Bayesian estimation are contrasted. Three loss functions such as the generalized quadratic, the Linex, and the entropy functions are used to derive the Bayesian estimators. All the estimation techniques provided have been evaluated through simulated studies. The BB algorithm is used to compare the censored maximum likelihood method to the Bayesian approach. With the aid of two applications and a simulation study, the construction of the Rao-Nikulin-Robson (RRN) statisticfor the new model in the uncensored case is explained in detail. Additionally, the development of the Rao-Robson-Nikulin statistic for the novel model under the censored situation is shown using data from twocensored applications and a simulation study.