Minimum mean squared estimation of location and scale parameters under misspecification of the model
Minimum mean squared estimation of location and scale parameters under misspecification of the model
复制标题
模型错误指定下位置和尺度参数的最小均方估计
DOI：
10.1093/biomet/68.2.501
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发表时间：
1981
期刊：
影响因子：
--
通讯作者：
M. Silvapulle
中科院分区：
文献类型：
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作者：
C. Heathcote;M. Silvapulle
本文研究的是通过最小化经验分布函数与方便选择的参数化分布函数之间的均方距离来估计位置和尺度参数的问题。在真参数族内，位置估计量具有与Hodges & Lehmann相同的渐近分布。如果底层分布不是假设参数族的成员，那么关于偏差的问题，而不是方差，是主要的，并且大部分论文都是关于这种情况的。给出了受污染的正态分布的数值结果。
SUMMARY The paper is concerned with estimating location and scale parameters by estimators minimizing a mean squared distance between the empirical distribution function and a conveniently chosen parameterized-distribution function. Within the true parametric family the location estimator has the same asymptotic distribution as that of Hodges & Lehmann. If the underlying distribution is not a member of the assumed parametric family, the questions concerning bias, rather than variance, are dominant and most of the paper is concerned with this situation. Numerical results are given for the case of contaminated normal distributions.