goodness-of-fit testing for the inverse rayleigh half-logistic distribution under progressive type-ii censoring‎: ‎entropy and pivotal quantity approaches

In this paper, the problem of goodness-of-fit testing for the inverse rayleigh-half-logistic distribution with a fixed shape parameter is investigated based on progressively type-ii censored samples. two test statistics are proposed using entropy-based approaches, along with a non-entropy-based test statistic constructed via the pivotal quantity method. the statistical properties of the suggested test procedures are thoroughly examined, and the corresponding critical values are obtained through monte carlo simulation. the power of the proposed tests is evaluated under various censoring schemes and against several alternative hypotheses. the results indicate that the proposed tests perform well overall, particularly against alternatives characterized by decreasing hazard rate functions. finally, a real data set is analyzed to illustrate the applicability of the proposed methods.