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Tests of fit for the Laplace distribution based on correcting moments of entropy estimators
Abstract:ABSTRACT

In this paper, we first consider the entropy estimators introduced by Vasicek A test for normality based on sample entropy. J R Statist Soc, Ser B. 1976;38:54–59], Ebrahimi et al. Two measures of sample entropy. Stat Probab Lett. 1994;20:225–234], Yousefzadeh and Arghami Testing exponentiality based on type II censored data and a new cdf estimator. Commun Stat – Simul Comput. 2008;37:1479–1499], Alizadeh Noughabi and Arghami A new estimator of entropy. J Iran Statist Soc. 2010;9:53–64], and Zamanzade and Arghami Goodness-of-fit test based on correcting moments of modified entropy estimator. J Statist Comput Simul. 2011;81:2077–2093], and the nonparametric distribution functions corresponding to them. We next introduce goodness-of-fit test statistics for the Laplace distribution based on the moments of nonparametric distribution functions of the aforementioned estimators. We obtain power estimates of the proposed test statistics with Monte Carlo simulation and compare them with the competing test statistics against various alternatives. Performance of the proposed new test statistics is illustrated in real cases.
Keywords:Information theory  goodness-of-fit test statistic  Kullback–Leibler information  nonparametric density function estimator
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