A new simple and powerful normality test for progressively Type-II censored data

TL;DR

This paper introduces a new goodness-of-fit test for normality tailored to progressively Type-II censored data, demonstrating superior power and consistency through simulation and real data application.

Contribution

The paper proposes a novel normality test specifically designed for progressively Type-II censored data, improving upon existing methods in power and consistency.

Findings

The new test is consistent and powerful based on Monte Carlo simulations.

It performs well on real data sets, outperforming existing tests.

Simulation results confirm its effectiveness for censored data.

Abstract

In this paper, a new goodness-of-fit test for a location-scale family based on progressively Type-II censored order statistics is proposed. Using Monte Carlo simulation studies, the present researchers have observed that the proposed test for normality is consistent and quite powerful in comparison with existing goodness-of-fit tests based on progressively Type-II censored data. Also, the new test statistic for a real data set is used and the results show that our new test statistic performs well.