• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2006년도 추계 학술대회
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• pp.221-226
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• 2006

We usually use two techniques(influence function and local influence) for detecting outliers. But, we cannot use these difficult techniques in elementary industrial statistics course for college students. We can use some simple graphical methods(box plot, dandelion seed plot, influence graph and cumulative deletion plot) for univariate and bivariate outlier detection and outlier effect in elementary industrial statistics course for college students.

• Communications for Statistical Applications and Methods
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• 제4권1호
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• pp.193-205
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• 1997

The multiprocess dynamic model provides a good framework for the modeling and analysis of the time series that contains outliers and is subject to abrupt changes in pattern. In this paper we consider the multiprocess discount generalized model with parameters having a dependent non-linear structure. This model has nice properties such as insensitivity to outliers and quick reaction to abrupt change of pattern in parameters.

• Communications for Statistical Applications and Methods
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• 제11권1호
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• pp.153-160
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• 2004

In recent years, the size of data set which we usually handle is enormous, so a lot of outliers could be included in data set. Therefore the robust procedures that automatically handle outliers become very importance issue. We consider the robust estimation problem of location parameter in the univariate case. In this paper, we propose a new method for defining robustness weights for the weighted mean based on the median distance of observations and compare its performance with several existing robust estimators by a simulation study. It turns out that the proposed method is very competitive.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 추계학술대회
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• pp.161-169
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• 2005

In this paper we propose a fuzzy c-regression model based on weighted least squares support vector machine(LS-SVM), which can be used to detect outliers in the switching regression model while preserving simultaneous yielding the estimates of outputs together with a fuzzy c-partitions of data. It can be applied to the nonlinear regression which does not have an explicit form of the regression function. We illustrate the new algorithm with examples which indicate how it can be used to detect outliers and fit the mixed data to the nonlinear regression models.

• Communications for Statistical Applications and Methods
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• 제16권2호
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• pp.309-315
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• 2009

We extended the results of Roy and Guria (2008) to multiple deletions in logistic regression models. Since single deletions may not exactly detect outliers or influential observations due to swamping effects and masking effects, it needs multiple deletions. We developed conditional deletion diagnostics which are designed to overcome problems of masking effects. We derived the closed forms for several statistics in logistic regression models. They give useful diagnostics on the statistics.

• Communications for Statistical Applications and Methods
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• 제9권2호
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• pp.473-478
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• 2002

A test for a single outlier in multivariate regression with linear constraints on regression coefficients using a mean shift model is derived. It is shown that influential observations based on case-deletions in testing linear hypotheses are determined by two types of outliers that are mean shift outliers with or without linear constraints, An illustrative example is given.

• Communications for Statistical Applications and Methods
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• 제7권3호
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• pp.633-641
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• 2000

In this paper, we propose a method to identify multiple outliers in regression analysis with only assumption of smoothness on the regression function. Our method uses single-linkage clustering algorithm and Projection Pursuit Regression (PPR). It was compared with existing methods using several simulated and real examples and turned out to be very useful in regression problem with the regression function which is far from linear.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2005년도 추계 학술발표회 논문집
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• pp.13-17
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• 2005

In recent years, many people use R as a statistics system. R is frequently updated by many R project teams. We are interested in the method of multiple outlier detection and know that R is not supplied the method of multiple outlier detection. In this talk, we review these procedures for detecting multiple outliers and provide more efficient procedures combined with direct methods and indirect methods using R.

• 품질경영학회지
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• 제19권2호
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• pp.17-39
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• 1991

In many application of response surface methodology, good estimation of the derivatives of the response function may be as important or perhaps more important than estimation of mean response. Using a graphical method, we have studied the effect of outliers, missing observations, and design augmentation with respect to the slope estimation in the response surf ace designs.

• 품질경영학회지
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• 제19권2호
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• pp.1-16
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• 1991

Recently, Cook and Weisberg(l989) presented dynamic graphics for regression diagnostics. They suggested animating graphics which could aid to understanding the effects of adding a variable to a model. In this paper, using the Cook and Weisberg's idea of animation, we propose a dynamic graphical method for residuals to display the effects of removing an observation from a model. Based on the information obtained from these animating graphics, it is possible to see the influence of outliers on influencial observations for regression diagnostics.