A Modified Chi-square Type Test for Distributional Validity with Applications to Right Censored Reliability and Medical Data

Haitham M Yousof,Mohamed Ibrahim Mohamed,Abdullah H Al-Nefaie,Khaoula Aidi,M Masoom Ali

doi:10.18187/pjsor.v17i4.3899

Abstract

In this paper, a modified Chi-square goodness-of-fit test called the modified Bagdonavičius-Nikulin goodness-of-fit test statistic is investigated and the applied for distributional validation under the right censored case. The new modified goodness-of-fit test is presented and applied for the right censored data sets. The algorithm of the censored Barzilai-Borwein is employed via a comprehensive simulation study for assessing validity of the new test. The modified Bagdonavičius-Nikulin test is applied to four real and right censored data sets. A new distribution is compared with many other competitive distributions under the new modified Bagdonavičius-Nikulin goodness-of-fit test statistic.

Highlights

Statistical methods for testing the validity of a parametric distribution are in increasing progress
The first data set has reported survival data on 26 psychiatric inpatients admitted to the university of Iowa hospitals during the years 1935-1948
The new modified goodness-of-fit test is presented based on the B-N Chi-square goodness-of-fit test

Summary

Introduction

Statistical methods for testing the validity of a parametric distribution are in increasing progress. A new modified type Chi-squared goodness-of-fit test statistic which is established based on the wellknown B-N test is presented and applied for distributional validation under the exponentiated Rayleigh exponentiated Nadarajah-Haghighi model using the right censored case. Yousof et al (2021c) introduced new modified Chi-square test for the right the censoring distributional validation under a novel Nadarajah Haghighi model with some new characterization results and different estimation methods. Another new version for the right censored validity under a new Chen model with some applications in reliability and medicine is presented by Ibrahim et al (2021)

Maximum likelihood estimation in censored data case

Findings

Conclusions