• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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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.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.457-469
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• 2007

Many procedures are available to identify a single outlier or an isolated influential point in linear regression and logistic regression. But the detection of influential points or multiple outliers is more difficult, owing to masking and swamping problems. The multiple outlier detection methods for logistic regression have not been studied from the points of direct procedure yet. In this paper we consider the direct methods for logistic regression by extending the $Pe\tilde{n}a$ and Yohai (1995) influence matrix algorithm. We define the influence matrix in logistic regression by using Cook's distance in logistic regression, and test multiple outliers by using the mean shift model. To show accuracy of the proposed multiple outlier detection algorithm, we simulate artificial data including multiple outliers with masking and swamping.

• Communications for Statistical Applications and Methods
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• v.3 no.2
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• pp.53-67
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• 1996

Numerous classical as well as robust methods have been proposed in the literature for the detection of multiple outlier in multivariate data. The effectiveness and power of each of these methods have not been thoroughly investigated. In this paper we first reduce the vast number of outlier detection methods to a small number of viable ones. This reduction is based on previous work of other researches and on some theoretical arguments. Then we design and implement a Monte Carlo experiment for comparing these methods. The main goal of our study is to determine which methods are most powerful in the detection of multiple outlier and in dealing with the masking and swamping problems. The results of the Monte Carlo study indicate that two of the methods seem to hace better performances than the others for the detection of multiple outlier in multivariate data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.122-129
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• 2010

High-performance concrete (HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other concrete ingredients. HPC is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed at demonstrating the possibilities of adapting artificial neural network (ANN) to predict the comprresive strength of HPC. Mahalanobis Distance (MD) outlier detection method used for the purpose increase prediction ability of ANN. The detailed procedure of calculating Mahalanobis Distance (MD) is described. The effects of outlier compared with before and after artificial neural network training. MD outlier detection method successfully removed existence of outlier and improved the neural network training and prediction performance.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.149-161
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• 2019

In this article, we suggest the following approaches to simultaneous variable selection and outlier detection. First, we determine possible candidates for outliers using properties of an intercept estimator in a difference-based regression model, and the information of outliers is reflected in the multiple regression model adding mean shift parameters. Second, we select the best model from the model including the outlier candidates as predictors using stochastic search variable selection. Finally, we evaluate our method using simulations and real data analysis to yield promising results. In addition, we need to develop our method to make robust estimates. We will also to the nonparametric regression model for simultaneous outlier detection and variable selection.

• Journal of the Korean Statistical Society
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• v.22 no.2
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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.

• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.635-647
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• 2013

Imputation is a commonly used method to handle missing survey data. The performance of the imputation method is influenced by various factors, especially an outlier. The removal of the outlier in a data set is a simple and effective approach to reduce the effect of an outlier. In this paper in order to improve the precision of multiple imputation, we study a imputation method which reduces the effect of outlier using various weight adjustment methods that include the removal of an outlier method. The regression method in PROC/MI in SAS is used for multiple imputation and the obtained final adjusted weight is used as a weight variable to obtain the imputed values. Simulation studies compared the performance of various weight adjustment methods and Monthly Labor Statistic data is used for real data analysis.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1229-1241
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• 2014

Most of sampling surveys have outliers and non-response missing values simultaneously. In that case, due to the effect of outliers, the result of imputation is not good enough to meet a given precision. To overcome this situation, outlier treatment should be conducted before imputation. In this paper in order for reducing the effect of outlier, we study outlier imputation methods and outlier weight adjustment methods. For the outlier detection, the method suggested by She and Owen (2011) is used. A small simulation study is conducted and for real data analysis, Monthly Labor Statistic and Briquette Consumption Survey Data are used.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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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.

• Journal of the Korean GEO-environmental Society
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• v.19 no.1
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• pp.5-14
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• 2018

All kinds of monitoring data in construction site could have outlier created from diverse cause. In this study generation technique of synthesis value, its regression, final outlier detection and assessment are conducted to distinct outlier data included in extensive time series dataset. Synthesis value having weight factor of correlation between a number of datasets consist of many monitoring data enable to detect outlier by increasing its correlation. Standard artificial dataset in which intentional outliers are inserted has been used for assessment of synthesis value technique. These results showed increase of detection accuracy for outlier and general tendency in case of having different time series models in common. Accuracy of outlier detection increased in case of using more dataset and showing similar time series pattern.