Least quantile squares method for the detection of outliers

자연과학 > 통계학
( Han Son Seo ) , ( Min Yoon )
CSAM(Communications for Statistical Applications and Methods) 2021년, 제28권 제1호, 81~88페이지(총8페이지)
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    k-least quantile of squares (k-LQS) estimates are a generalization of least median of squares (LMS) estimates. They have not been used as much as LMS because their breakdown points become small as k increases. But if the size of outliers is assumed to be fixed LQS estimates yield a good fit to the majority of data and residuals calculated from LQS estimates can be a reliable tool to detect outliers. We propose to use LQS estimates for separating a clean set from the data in the context of outlyingness of the cases. Three procedures are suggested for the identification of outliers using LQS estimates. Examples are provided to illustrate the methods. A Monte Carlo study show that proposed methods are effective.
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