新的$L^2$型指数检验。

Marija Cupari'c, Bojana Milovsevi'c, Marko Obradovi'c
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引用次数: 18

摘要

我们引入了新的基于Puri-Rubin表征的指数分布的一致和无标度的拟合优度检验。对于检验统计量的构造,我们采用加权的$L^2$ V$之间的距离-表征中出现的随机变量的经验拉普拉斯变换。得到的测试统计量是退化的带有估计参数的v统计量。就Bahadur效率而言,我们将我们的测试与似然比测试以及最近一些基于指数分布特征的拟合优度测试进行了比较。我们还将我们的测试的幂与一些最近的和经典的指数测试的幂进行了比较。在这两项标准中,我们的测试都表现得很出色,超过了大多数竞争对手。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New $L^2$-type exponentiality tests.
We introduce new consistent and scale-free goodness-of-fit tests for the exponential distribution based on Puri-Rubin characterization. For the construction of test statistics we employ weighted $L^2$ distance between $V$-empirical Laplace transforms of random variables that appear in the characterization. The resulting test statistics are degenerate V-statistics with estimated parameters. We compare our tests, in terms of the Bahadur efficiency, to the likelihood ratio test, as well as some recent characterization based goodness-of-fit tests for the exponential distribution. We also compare the powers of our tests to the powers of some recent and classical exponentiality tests. In both criteria, our tests are shown to be strong and outperform most of their competitors.
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