• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.313-323
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• 2003

For the growth curve model with arbitrary covariance structure, known as unstructured covariance matrix, the problems of detecting outliers are discussed in this paper. In order to detect outliers in the growth curve model, the test statistics using U-distribution is established. After detecting outliers in growth curve model, we test homo and/or hetero-geneous covariance matrices using PSR Quasi-Bayes Criterion. For illustration, one numerical example is discussed, which compares between before and after outlier deleting.

• Journal of Korean Society for Quality Management
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• v.21 no.1
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• pp.55-64
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• 1993

This article is concerned with procedures for detecting one or more outliers or influential observations in a linear regression model. A test procedure, based on recursive residuals is proposed and developed The power of the test procedure to identify one or more outliers is investigated through simulation, and its relevance to the number and configuration of the outlier.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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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.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.21-32
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• 2002

We searched out in the votes data of the State of Florida at presidential election 2000. We used a multivariate regression analysis. We got there were several outliers including Palm Beach County. It means that we should analyze the number of disqualified ballots which were double-punched as well as the votes, to insist the " Butterfly Ballot" made Palm Beach outlier.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.3
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• pp.74-80
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• 2012

In resolution-constrained quantization, the size of Voronoi cell varies depending on probability density function of the input data, which causes large amount of distortion outliers. We propose a vector quantization method that reduces distortion outliers by combining the generalized Lloyd algorithm (GLA) and the cell-size constrained vector quantization (CCVQ) scheme. The training data are divided into the inside and outside regions according to the size of Voronoi cell, and consequently CCVQ and GLA are applied to each region, respectively. As CCVQ is applied to the densely populated region of the source instead of GLA, the number of centroids for the outside region can be increased such that distortion outliers can be decreased. In real-world environment, source mismatch between training and test data is inevitable. For the source mismatch case, the proposed algorithm improves performance in terms of average distortion and distortion outliers.

• Ocean and Polar Research
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• v.38 no.2
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• pp.149-159
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• 2016

The statistical measures of the coastal environmental data are used in a variety of statistical inferences, hypothesis tests, and data-driven modeling. If the measures are biased, then the statistical estimations and models may also be biased and this potential for bias is great when data contain some outliers defined as extraordinary large or small data values. This study aims to suggest more robust statistical measures as alternatives to more commonly used measures and to assess the performance these robust measures through a quantitative evaluation of more typical measures, such as in terms of locations, spreads, and shapes, with regard to environmental monitoring data in the Busan coastal sea. The detection of outliers within the data was carried out on the basis of Rosner's test. About 5-10% of the nutrient data were found to contain outliers based on Rosner's test. After removal (zero-weighting) of the outliers in the data sets, the relative change ratios of the mean and standard deviation between before and after outlier-removal conditions revealed the figures 13 and 33%, respectively. The variation magnitudes of skewness and kurtosis are 1.36 and 8.11 in a decreasing trend, respectively. On the other hand, the change ratios for more robust measures regarding the mean and standard deviation are 3.7-10.5%, and the variation magnitudes of robust skewness and kurtosis are about only 2-4% of the magnitude of the non-robust measures. The robust measures can be regarded as outlier-resistant statistical measures based on the relatively small changes in the scenarios before and after outlier removal conditions.

• The Korean Journal of Applied Statistics
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• v.9 no.2
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• pp.109-117
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• 1996

We introduce the more informative sequential probability ratio test(SPRT) than currently used SPRT. Though the proposed SPRT shares similar mathematical properties with the ordinary SPRT, it is less affected by the outliers and even it indicates possible existence of such outliers. Futhermore, it responds to the changes among observations more quickly than the ordinary SPRT.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.271-284
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• 2002

In this paper we consider the robust inference for the parameter of linear regression model based on weighted least squares. First we consider the sequential test of multiple outliers. Next we suggest the way to assign a weight to each observation $(x_i,\;y_i)$ and recommend the robust inference for linear model. Finally, to check the performance of confidence interval for the slope using proposed method, we conducted a Monte Carlo simulation and presented some numerical results and examples.

• 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.

• Journal of Korean Society on Water Environment
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• v.26 no.4
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• pp.700-706
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• 2010

Along the 4 major rivers in korea, there are automatic water quality monitoring (AWQM) stations to immediately respond to any pollution incident. Real-time data (temperature, DO, pH, EC and TOC) collected at each station were statistically treated to exclude outliers and keep valid data using Dixon's test and Discordance test. These applied methods were compared in terms of the number of the outliers sorted out. There was no significant difference between these methods. On the other hand, more outliers were sorted out from EC and TOC data, comparing with other water quality items. EC data did not show partly any variation for a long time at H station. If measured signal does not exceed ${\pm}0.001mS/cm$ from the sectional mean, the signal should be treated as normal data. Therefore, another routine was added to the data screening system, some data which were removed as outlier were restored.