Bias‐calibrated estimation from sample surveys containing outliers
Bias‐calibrated estimation from sample surveys containing outliers
复制标题
来自包含异常值的样本调查的偏差校准估计
DOI：
10.1111/1467-9868.00133
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发表时间：
1998
期刊：
影响因子：
--
通讯作者：
E. Ronchetti
中科院分区：
文献类型：
--
作者：
A. Welsh;E. Ronchetti
本文讨论了在含有代表性异常值的样本的基础上估计有限总体参数的问题。我们澄清了钱伯斯的偏差校准估计的总体总量的动机，并表明偏差校准是一个关键的想法，在构建估计的有限总体参数。然后，我们联系起来的问题，估计人口总数的分布函数或分位数估计，并探讨了一种方法的基础上使用钱伯斯的估计。我们还提出了基于使用稳健估计和人口分布函数的Chambers和Dunstan估计的偏差校准形式的方法。该建议导致总体的偏差校准估计量，这是Chambers的替代方案。我们提出了一个小的模拟研究来说明这些估计的效用。
We discuss the problem of estimating finite population parameters on the basis of a sample containing representative outliers. We clarify the motivation for Chambers's bias‐calibrated estimator of the population total and show that bias calibration is a key idea in constructing estimators of finite population parameters. We then link the problem of estimating the population total to distribution function or quantile estimation and explore a methodology based on the use of Chambers's estimator. We also propose methodology based on the use of robust estimates and a bias‐calibrated form of the Chambers and Dunstan estimator of the population distribution function. This proposal leads to a bias‐calibrated estimator of the population total which is an alternative to that of Chambers. We present a small simulation study to illustrate the utility of these estimators.