• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.519-524
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• 2003

We suggest a method of identifying outliers, using local influence, in regression when linear constraints are imposed on the regression coefficients. An example is given for illustration.

• 응용통계연구
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• 제15권2호
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• pp.269-279
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• 2002

다중 이상점 과 다중 지렛점의 식별은 가장효과(masking effect)와 편승효과(swamping effect)에 영향을 받으므로 어려움이 존재한다. Rousseeuw와 van Zomeren(1990)은 LMS (Least Median of Squares) 회귀방법과 MVE(Minimum Volume Ellipsoid) 통계량을 이용하여 다중 이상점과 다중 지렛점을 식별하였다. 그러나 이들의 방법은 LMS와 MVE의 강한 로버스트성으로 인하여 이상점과 지렛점이 아닌 점들도 이상점과 지렛점으로 식별하는 경향이 있다. Fung(1993)은 식별된 이상점과 지렛점들에 대하여 재확인방법을 제안하였는데 이 방법은 인근효과(adjacent effect)에 영향을 받아 이상점과 지렛점을 식별하는데 문제가 있는 것으로 분석되었다. 본 논문은 이러한 문제점을 지적하고 새로운 방법을 제안하여 식별된 이상점과 지렛점을 재확인하고자 한다.

• 응용통계연구
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• 제24권5호
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• pp.979-986
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• 2011

선형회귀모형분석은 방법의 간편성과 높은 적용성에 의해 다양한 종류의 자료 분석에 활용되고 있다. 하지만 자료에 이상치가 포함되는 경우 이에 민감하게 영향을 받게 되므로 의심되는 관찰치를 찾아서 이상치 여부를 검토하는 것이 중요하다. 그러나 이상치를 탐지하는 방법의 대부분은 가면화 효과 등 이상치로부터 영향을 받아 정확하게 이상치를 발견하지 못하는 경우가 있다. 본 연구에서는 이를 개선하기 위하여 동적 잔차도를 활용한 방법을 제안한다. 제안된 방법은 종속적 이상치탐지방법을 사용할 때 다양한 기초군을 제공하는데 유용하며 결과적으로 정확한 이상치군을 탐지하게 되는 것을 예를 통해 검증한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1499-1502
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• 2002

A method for acquiring an environmental map by integrating distance data obtained by sonars of a moving robot with web interface is proposed. Sonar data contains outliers in some cases such as ultrasonic beam is projected onto a corner of an object. Therefore, the influence of the outliers should be reduced by detecting outliers. In our method, the outliers are detected by two ways: (i) a method considering geometrical .elation among the observed surface and the projected ultrasonic beau, and (ii) a method considering consistency with data obtained by other sonars. By measurement by the sonar, the distance from the sonar to the obstacle is obtained. Assuming the two dimensional space we can know that the inside of the sector, whose renter coincide with the sonar and whose radius is equal to the obtained distance, is the free area, and a part of the arc of this sector is the obstacle area. The generation of the environmental map is done by integrating the free area and the obstacle area obtained by each measurement by the sonars. Before the integration, the outliers detection is done by two ways mentioned above. Experimental results show that obtained maps obtained by our methods with outliers defection are much better than those by a method without outliers detection.

• Genomics & Informatics
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• 제18권1호
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• pp.2.1-2.11
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• 2020

In this paper, we propose a new approach to detecting outliers in a set of segmented genomes of the flu virus, a data set with a heterogeneous set of sequences. The approach has the following computational phases: feature extraction, which is a mapping into feature space, alignment-free distance measure to measure the distance between any two segmented genomes, and a mapping into distance space to analyze a quantum of distance values. The approach is implemented using supervised and unsupervised learning modes. The experiments show robustness in detecting outliers of the segmented genome of the flu virus.

• Journal of applied mathematics & informatics
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• 제4권2호
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• pp.501-512
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• 1997

In multiple linear regression model we have presupposed assumptions (independence normality variance homogeneity and so on) on error term. When case weights are given because of variance heterogeneity we can estimate efficiently regression parameter using weighted least squares estimator. Unfortunately this estimator is sen-sitive to outliers like ordinary least squares estimator. Thus in this paper we proposed some statistics for detection of outliers in weighted least squares regression.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2000년도 추계학술발표회 논문집
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• pp.173-179
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• 2000

Genetic algorithm(GA) is applied for detecting multiple outliers. GA is a heuristic optimization tool solving for near optimal solution. We compare the performance of GA and the other diagnostic measures commonly used for detecting outliers in regression model. The results show that GA seems to have better performance than the others for the detection of multiple outliers.

• 응용통계연구
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• 제25권3호
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• pp.503-512
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• 2012

Detecting outliers in a linear regression model eventually fails when similar observations are classified differently in a sequential process. In such circumstances, identifying clusters and applying certain methods to the clustered data can prevent a failure to detect outliers and is computationally efficient due to the reduction of data. In this paper, we suggest to implement a clustering procedure for this purpose and provide examples that illustrate the suggested procedure applied to the Hadi-Simonoff (1993) method, reverse Hadi-Simonoff method, and Gentleman-Wilk (1975) method.

• Communications for Statistical Applications and Methods
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• 제11권1호
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• pp.189-196
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• 2004

The sensitivity of the multivariate Box-Cox transformation model to simultaneous perturbations of all the data based on the likelihood displacement is studied in order to detect outliers. An example is given for illustration.

• Communications for Statistical Applications and Methods
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• 제2권2호
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• pp.184-197
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• 1995

An objective and one-step detection procedure of multiple outliers is suggested by using the gaps of the order statistics. The detection procedure can be used as a routine outlier detection method of a statistical analysis computer program. The procedure is applied to some examples including the data selected by Kitagawa.