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

  • Salwa L. AlKhayyat 1Department of Statistics, Faculty of Science, University of Jeddah, Kingdom of Saudi Arabia; 2Department of Statistics, Mathematics and Insurance, Faculty of Commerce, Kafr El-Sheikh University, Egypt
  • Haitham M. Yousof Department of Statistics, Mathematics and Insurance, Faculty of Commerce, Benha University, Benha 13518, Egypt
  • Hafida Goual Laboratory of probability and statistics LaPS, University Badji Mokhtar, Annaba, Algeria
  • Talhi Hamida Laboratory of probability and statistics LaPS, University Badji Mokhtar, Annaba, Algeria
  • Mohamed S. Hamed 1Department of Statistics, Mathematics and Insurance, Faculty of Commerce, Benha University, Benha 13518, Egypt; 2Department of Business Administration, Gulf Colleges, KSA
  • Aiachi Hiba Laboratory of probability and statistics LaPS, University Badji Mokhtar, Annaba, Algeria
  • Mohamed Ibrahim Department of Quantitative Methods, School of Business, King Faisal University, Al Ahsa 31982, Saudi Arabia
DOI: https://doi.org/10.19139/soic-2310-5070-1710
Keywords: Bayesian Estimation; BB algorithm; Censored Applications; Lomax Model; Nikulin-Rao-Robson; Pitman’s Proximity

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.
Published
2025-02-24
How to Cite
AlKhayyat, S. L., Haitham M. Yousof, Hafida Goual, Hamida, T., Hamed, M. S., Hiba, A., & Mohamed Ibrahim. (2025). Rao-Robson-Nikulin Goodness-of-fit Test Statistic for Censored and Uncensored Real Data with Classical and Bayesian Estimation. Statistics, Optimization & Information Computing, 13(6), 2205-2225. https://doi.org/10.19139/soic-2310-5070-1710
Issue
Vol 13 No 6 (2025)
Section
Research Articles
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