• Journal of the Korean Data and Information Science Society
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• 제11권2호
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• pp.201-215
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• 2000

The classical method for regression analysis is the least squares method. However, if the data contain significant outliers, the least squares estimator can be broken down by outliers. To remedy this problem, the robust methods are important complement to the least squares method. Robust methods down weighs or completely ignore the outliers. This is not always best because the outliers can contain some very important information about the population. If they can be detected, the outliers can be further inspected and appropriate action can be taken based on the results. In this paper, I propose a sequential outlier test to identify outliers. It is based on the nonrobust estimate and the robust estimate of scatter of a robust regression residuals and is applied in forward procedure, removing the most extreme data at each step, until the test fails to detect outliers. Unlike other forward procedures, the present one is unaffected by swamping or masking effects because the statistics is based on the robust regression residuals. I show the asymptotic distribution of the test statistics and apply the test to several real data and simulated data for the test to be shown to perform fairly well.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.1053-1066
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• 2005

In this paper we consider the problem of identifying and testing outliers in linear regression. First we consider the use of the so-called scale ratio tests for testing the null hypothesis of no outliers. This test is based on the ratio of two residual scale estimates. We show the asymptotic distribution of the test statistics and investigate its properties. Next we consider the problem of identifying the outliers. A forward sequential procedure using the suggested test is proposed. The new method is compared with classical procedure in the real data example. Unlike other forward procedures, the present one is unaffected by masking and swamping effects because the test statistic is based on robust scale estimate.

• Journal of the Korean Data and Information Science Society
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• 제15권4호
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• pp.921-934
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• 2004

We consider the problem of identifying and testing outliers in linear regression. First, we consider the scale-ratio tests for testing the null hypothesis of no outliers. A test based on the ratio of two residual scale estimates is proposed. We show the asymptotic distribution of test statistics and investigate the properties of the test. Next we consider the problem of identifying the outliers. A forward procedure based on the suggested test is proposed and shown to perform fairly well. The forward procedure is unaffected by masking and swamping effects because the test statistics used a robust scale estimate.

• 응용통계연구
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• 제30권1호
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• pp.159-167
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• 2017

일반적으로 잠재적 이상치군은 검정과정을 통해 최종적으로 이상치 여부를 판단하지만 검정절차를 생략하거나 모의실험에 의해 계산된 유의값을 기반으로 검정을 수행하는 이상치 탐지법들도 있다. 본 논문에서는 가면화나 수렁화현상을 피하기 위하여 이상치후보군에 속한 개별 관찰치를 검정하지 않고 이상치후보군의 부분집합들을 검정하는 절차를 제안한다. 제안된 방법의 활용을 보여주는 예제와 다른 방법과의 검정력 비교를 위한 모의실험 결과가 제시된다.

• 한국컴퓨터정보학회논문지
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• 제28권5호
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• pp.179-187
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• 2023

본 논문은 크론바흐 알파 신뢰계수의 유의성 검정에서 이상치가 검정력에 미치는 영향을 연구한 것이다. 표본 크기, 문항들의 수, 이상치의 수, 모집단의 크론바흐 알파 레벨의 네 개의 변수들에 변화를 주었다. 데이터 시물에이션을 위해 다변량 정규분포를 사용했고 균일분포로부터 이상치를 추출하여 사용했다. 크론바흐 알파 신뢰도의 유의성 검정을 위해 모수적 검정(F 검정)과 퍼뮤테이션 검정을 사용하였다. 결과적으로 퍼뮤테이션 검정의 검정력은 F검정의 검정력 보다 크거나 같았고, 두 검정의 검정력은 모두 이상치의 수가 많아질수록 감소하였으며 이러한 이상치의 영향은 모집단의 알파 레벨이 증가할수록 크게 나타났다.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.337-346
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• 2001

In this paper we consider the problem of identifying and testing the outliers in linear regression. first we consider the problem for testing the null hypothesis of no outliers. The test based on the ratio of two scale estimates is proposed. We show the asymptotic distribution of the test statistic by Monte Carlo simulation and investigate its properties. Next we consider the problem of identifying the outliers. A forward sequential procedure based on the suggested test is proposed and shown to perform fairly well. The forward sequential procedure is unaffected by masking and swamping effects because the test statistic is based on robust estimate.

• Journal of the Korean Data and Information Science Society
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• 제14권3호
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• pp.673-685
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• 2003

In this paper we consider the problem of identifying and testing outliers in linear regression. First we consider the problem for testing the null hypothesis of no outliers. A test based on the ratio of two residual scale estimates is proposed. We show the asymptotic distribution of the test statistics by Monte Carlo simulation and investigate its properties. Next we consider the problem of identifying the outliers. A forward sequential procedure using the suggested test is proposed and shown to perform fairly well. Unlike other forward procedures, the present one is unaffected by masking and swamping effects because the test statistic is based on robust scale estimate.

• Journal of the Korean Data and Information Science Society
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• 제15권1호
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• pp.129-139
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• 2004

We consider the problem of testing for outliers in generalized linear model. We proceed by first specifying a mean shift outlier model, assuming the suspect set of ourliers is known. Given this model, we discuss standard approaches to obtaining score test for outliers as an alternative to the likelihood ratio test.

• Journal of the Korean Statistical Society
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• 제22권2호
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• pp.201-208
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• 1993

Given the specific mean shift outlier model, the score test for multiple outliers in nonlinear regression is discussed as an alternative to the likelihood ratio test. The geometric interpretation of the score statistic is also presented.

• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.419-437
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• 1995

Given the specific mean shift outlier model, several standard approaches to obtaining test statistic for outliers are discussed. Each of these is developed in detail for the nonlinear regression model, and each leads to an equivalent distribution. The geometric interpretations of the statistics and accuracy of linear approximation are also presented.