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) statistic for 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 two censored
applications and a simulation study.