Shrinkage Estimation and Bootstrap Confidence Interval for Scale Parameter of Laplace Distribution

Ş. Özdemir, M. Ebegil
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Abstract

In this study, a biased estimator is proposed for the scale parameter of Laplace distribution. First, it is theoretically shown that the mean square error of the biased estimator is smaller than that of the maximum likelihood estimator. Then the maximum likelihood estimator is compared with the obtained biased estimator by means of a simulation study using the relative efficiency of these estimators. In addition, confidence intervals are constructed for the scale parameter of Laplace distribution with bootstrap method in order to compare them with each other in a different way.
拉普拉斯分布尺度参数的收缩估计和自举置信区间
本文提出了拉普拉斯分布尺度参数的有偏估计。首先,从理论上证明了偏估计量的均方误差小于极大似然估计量的均方误差。然后利用最大似然估计量与得到的偏估计量的相对效率进行了仿真研究。此外,用自举法对拉普拉斯分布的尺度参数构造置信区间,以不同的方式进行相互比较。
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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