Testing Skew-Laplace Distribution Using Density-based Empirical Likelihood Approach

M. Safavinejad, S. Jomhoori, H. A. Noughabi
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引用次数: 1

Abstract

. In this paper, we first describe the skew-Laplace distribution and its properties. We then introduce a goodness of fit test for this distribution according to the density-based empirical likelihood ratio concept. Asymptotic consistency of the proposed test is demonstrated. The critical values and Type I error of the test are obtained by Monte Carlo simulations. More-over, the empirical distribution function (EDF) tests are considered for the skew-Laplace distribution to show they do not have acceptable Type I error in comparison with the proposed test. Results show that the proposed test has an excellent Type I error which does not depend on the unknown parameters. The results obtained from simulation studies designed to investigate the power of the test are presented, too. The applicability of the proposed test in practice is demonstrated by real data examples.
基于密度的经验似然法检验斜拉普拉斯分布
. 本文首先描述了斜拉普拉斯分布及其性质。然后,根据基于密度的经验似然比概念,对该分布引入拟合优度检验。证明了所提检验的渐近一致性。通过蒙特卡罗模拟得到了试验的临界值和I型误差。此外,还考虑了偏拉普拉斯分布的经验分布函数(EDF)检验,以表明它们与提议的检验相比没有可接受的I型误差。结果表明,该方法具有良好的I型误差,不依赖于未知参数。本文还介绍了为研究该测试的有效性而进行的模拟研究的结果。通过实际数据算例验证了该方法在实际应用中的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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