• 제목/요약/키워드: mean-shift outlier model

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Testing Outliers in Nonlinear Regression

  • Kahng, Myung-Wook
    • Journal of the Korean Statistical Society
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    • 제24권2호
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    • pp.419-437
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    • 1995
  • Given the specific mean shift outlier model, several standard approaches to obtaining test statistic for outliers are discussed. Each of these is developed in detail for the nonlinear regression model, and each leads to an equivalent distribution. The geometric interpretations of the statistics and accuracy of linear approximation are also presented.

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Assessing the Accuracy of Outlier Tests in Nonlinear Regression

  • Kahng, Myung-Wook;Kim, Bu-Yang
    • Communications for Statistical Applications and Methods
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    • 제16권1호
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    • pp.163-168
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    • 2009
  • Given the specific mean shift outlier model, the standard approaches to obtaining test statistics for outliers are discussed. Accuracy of outlier tests is investigated using subset curvatures. These subset curvatures appear to be reliable indicators of the adequacy of the linearization based test. Also, we consider obtaining graphical summaries of uncertainty in estimating parameters through confidence curves. The results are applied to the problem of assessing the accuracy of outlier tests.

A Score test for Detection of Outliers in Nonlinear Regression

  • Kahng, Myung-Wook
    • Journal of the Korean Statistical Society
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    • 제22권2호
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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.

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Test for an Outlier in Multivariate Regression with Linear Constraints

  • Kim, Myung-Geun
    • 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.

Simultaneous outlier detection and variable selection via difference-based regression model and stochastic search variable selection

  • Park, Jong Suk;Park, Chun Gun;Lee, Kyeong Eun
    • Communications for Statistical Applications and Methods
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    • 제26권2호
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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.

Asymptotic Properties of Outlier Tests in Nonlinear Regression

  • Kahng, Myung-Wook
    • Journal of the Korean Data and Information Science Society
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    • 제17권1호
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    • pp.205-211
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    • 2006
  • For a linear regression model, the necessary and sufficient condition for the asymptotic consistency of the outlier test statistic is known. An analogous condition for the nonlinear regression model is considered in this paper.

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Bayesian Outlier Detection in Regression Model

  • Younshik Chung;Kim, Hyungsoon
    • Journal of the Korean Statistical Society
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    • 제28권3호
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    • pp.311-324
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    • 1999
  • The problem of 'outliers', observations which look suspicious in some way, has long been one of the most concern in the statistical structure to experimenters and data analysts. We propose a model for an outlier problem and also analyze it in linear regression model using a Bayesian approach. Then we use the mean-shift model and SSVS(George and McCulloch, 1993)'s idea which is based on the data augmentation method. The advantage of proposed method is to find a subset of data which is most suspicious in the given model by the posterior probability. The MCMC method(Gibbs sampler) can be used to overcome the complicated Bayesian computation. Finally, a proposed method is applied to a simulated data and a real data.

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평균이동모형을 이용한 성장곡선모형의 이상점 진단에 관한 연구 (Outlier Detection in Growth Curve Model Using Mean-Shift Model)

  • 심규박
    • Journal of the Korean Data and Information Science Society
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    • 제10권2호
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    • pp.369-385
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    • 1999
  • 성장곡선모형에서 다중 이상값들이나 영향관측값들을 탐지하는 문제는 선형회귀모형에서의 문제에 비해 매우 복잡하여 거의 이루어지지 않고 있는 실정이다. 본 연구에서는 이상점을 포함하고 있는 성장곡선모형에서 이들을 탐지하는 방법으로 평균이동모형을 이용하는 방법을 소개하였다. 이 방법을 이용하여 찾아낸 자료가 이상점인지의 여부를 예측표본재이용 의사 베이즈 우도 기준법을 이용한 등분산성의 검정을 통해 알아보았다. 끝으로 Potthoff(1964)등이 사용한 자료를 이용한 예제를 통해 이상점 탐지와 등분 산성 검정을 실시한 결과를 제시하였다.

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Accuracy of Multiple Outlier Tests in Nonlinear Regression

  • Kahng, Myung-Wook
    • Communications for Statistical Applications and Methods
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    • 제18권1호
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    • pp.131-136
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    • 2011
  • The original Bates-Watts framework applies only to the complete parameter vector. Thus, guidelines developed in that framework can be misleading when the adequacy of the linear approximation is very different for different subsets. The subset curvature measures appear to be reliable indicators of the adequacy of linear approximation for an arbitrary subset of parameters in nonlinear models. Given the specific mean shift outlier model, the standard approaches to obtaining test statistics for outliers are discussed. The accuracy of outlier tests is investigated using subset curvatures.

A Score Test for Detection of Outliers in Generalized Linear Models

  • Kahng, Myung-Wook;Kim, Min-Kyung
    • Journal of the Korean Data and Information Science Society
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    • 제15권1호
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    • pp.129-139
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    • 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.

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