• Journal of the Korean Data and Information Science Society
• /
• 제16권4호
• /
• pp.979-987
• /
• 2005

In this research we study conventional and new statistical methods to analyse and detect outliers in network traffic and we apply the nonlinear time series model to make better performance of detecting abnormal traffic rather the linear time series model to compare the performances of the two models.

• Communications for Statistical Applications and Methods
• /
• 제18권6호
• /
• pp.817-835
• /
• 2011

The moment(MM) and least squares(LS) estimations of the parameters are derived for the Pareto distribution in the presence of outliers. Further, we have derived a mixture method(MIX) of estimations with MM and LS that shows that the MIX is more efficient. In the final section we have given an example of actual data from a medical insurance company.

• Communications for Statistical Applications and Methods
• /
• 제18권6호
• /
• pp.733-739
• /
• 2011

The linear absolute shrinkage and selection operator(Lasso) method improves the low prediction accuracy and poor interpretation of the ordinary least squares(OLS) estimate through the use of $L_1$ regularization on the regression coefficients. However, the Lasso is not robust to outliers, because the Lasso method minimizes the sum of squared residual errors. Even though the least absolute deviation(LAD) estimator is an alternative to the OLS estimate, it is sensitive to leverage points. We propose a robust Lasso estimator that is not sensitive to outliers, heavy-tailed errors or leverage points.

• 품질경영학회지
• /
• 제15권1호
• /
• pp.63-67
• /
• 1987

Basic problems in the study of detecting outliers from data of experimental designs are that they are difficult to detect and their presence influences the analysis of variance of the data set. This article is concerned with mainly detecting outliers in two-way tables with no replications. Various methods are reviewed and their relations to the Andrews-Pregibon's Statistic and Cook's Statistic are derived.

• 응용통계연구
• /
• 제15권1호
• /
• pp.21-32
• /
• 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 Korean Data and Information Science Society
• /
• 제15권1호
• /
• pp.129-139
• /
• 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
• /
• 제20권2호
• /
• pp.147-155
• /
• 1991

This paper treats a robust design criterion which minimizes the effects of outliers and model inadequacy, and investigates robust designs for some response surface designs. In order to develop a robust design criterion and robust design, the integrated mean squared error of *(equation omitted) over a region is utilized, where *(equation omitted). is the estimated response by the minimum bias estimation proposed by carson, Manson and Hader (1969) . According to the number of aberrant observations and their positions, the proposed criterion and designs are studied. Also further development of the proposed criterion is treated when outliers can occur in any position of a design.

• Communications for Statistical Applications and Methods
• /
• 제26권6호
• /
• pp.575-582
• /
• 2019

The problem of selecting variables in the presence of outliers is considered. Variable selection and outlier detection are not separable problems because each observation affects the fitted regression equation differently and has a different influence on each variable. We suggest a simultaneous method for variable selection and outlier detection in a linear regression model. The suggested procedure uses a sequential method to detect outliers and uses all possible subset regressions for model selections. A simplified version of the procedure is also proposed to reduce the computational burden. The procedures are compared to other variable selection methods using real data sets known to contain outliers. Examples show that the proposed procedures are effective and superior to robust algorithms in selecting the best model.

• 한국농공학회지
• /
• 제45권5호
• /
• pp.140-150
• /
• 2003

Observational methods such as the Asaoka's method and the hyperbolic method are widely applied on the settlement analysis using observed settlement. The most unreliable aspects in those methods is arose from the subjective discretion of initial non-linearity on linear regression. The initial non-linearity is inevitable due to the settlement behaviour itself. Therefore an objective method is essential to achieve more reliable results on settlement analysis. It was found that the initial non-linear data are statistical outliers. New automation algorithms of the hyperbolic and the Asaoka's method were developed based on outlier detection method. The methods are a successive detection of outliers and a searching method of suitable hyperbolic range for the Asaoka's and the hyperbolic method respectively. Applicability of the algorithms was verified through case studies.

• 응용통계연구
• /
• 제6권1호
• /
• pp.67-78
• /
• 1993

회귀진단에서 이상치와 영향을 많이 주는 측정치를 발견하는 새로운 통계량을 제안하였다. 이 제안된 통계량은 이상치를 찾는 측도와 영향추정치를 찾는 측도의 가중함으로 해석될 수 있으며, 가중치를 변화시킴으로써 이상치와 영향추정치들을 일목요연하게 찾아낼 수 있다는 장점이 있다. 씨뮬레이션을 이용하여 제안된 통계량의 분포형태를 살펴 보았다.