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In the present paper, we develop a new goodness of fit test for gamma distribution based on the fixed point characterization. We use U-Statistics theory to derive the test statistic. We discuss how the right censored observations are incorporated into the proposed test. The asymptotic properties of the test statistics in censored and uncensored cases are studied. We carry out a Monte Carlo simulation study to validate the finite sample performance of the proposed tests. We also illustrate the test procedures using real data sets. [ABSTRACT FROM AUTHOR]

In this paper, we study a replacement model under which replacements take place not at fixed time but at random time T or upon failure. We study the properties of mean time to failure of the proposed model. Life times of two replacement models with different random replacement times have been compared using few stochastic orderings. A non-parametric test based on U-Statistics has been proposed for testing constancy of mean time to failure against NBUE alternative. The finite sample performance of the proposed test is evaluated through Monte Carlo simulation study. Finally, the proposed test procedure is illustrated using lifetime data of air conditioning equipment. [ABSTRACT FROM AUTHOR]

Sepehrifar and Yarahmadian (Stat Pap 58:1115–1124, 2017) had developed a non-parametric test for testing exponentiality against decreasing renewal dichotomous Markov noise shock model (DRDMNS) alternatives which when subjected to scrutiny under delivers. Hence, we propose a non-parametric test for testing exponentiality against a class of distributions belonging to DRDMNS models. The asymptotic properties of the test statistic are discussed. An exact null distribution is derived and critical values with different sample sizes are obtained. The proposed test is applied to the censored data also. The results of the Monte Carlo simulations are used to further manifest the quality of the proposed test. Finally, the proposed test is illustrated using two real data sets. [ABSTRACT FROM AUTHOR]

Competing risks data with discrete lifetime come up in practice. However, only limited literature exists for such data. In this paper, we propose a non-parametric test based on U-statistics for testing independence of time to failure and cause of failure of competing risks data when the lifetime is a discrete random variable. Asymptotic distribution of the proposed test statistic is derived. An extensive Monte Carlo simulation study is conducted to assess the finite sample performance of the proposed test. The flexibility of the testing procedure is illustrated using real data sets on oral cancer patients and drug-exposed pregnancies. [ABSTRACT FROM AUTHOR]