Communications for Statistical Applications and Methods

  • 제28권1호
  • /
  • Pages.81-88
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  • 2021
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Least quantile squares method for the detection of outliers

  • Seo, Han Son (Department of Applied Statistics, Konkuk University) ;
  • Yoon, Min (Department of Applied Mathematics, Pukyong National University)
  • 투고 : 2020.10.07
  • 심사 : 2020.11.09
  • 발행 : 2021.01.31
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초록

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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참고문헌

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