• 제목/요약/키워드: high breakdown estimator

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An Efficient Mallows-Type One-Step GM-Estimator in linear Models

  • Song, Moon-Sup;Park, Changsoon;Nam, Ho-Soo
    • Journal of the Korean Statistical Society
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    • 제27권3호
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    • pp.369-383
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    • 1998
  • This paper deals with a robust regression estimator. We propose an efficient one-step GM-estimator, which has a bounded influence function and a high breakdown point. The main idea of this paper is to use the Mallows-type weights which depend on both the predictor variables and the residuals from a high breakdown initial estimator. The proposed weighting scheme severely downweights the bad leverage points and slightly downweights the good leverage points. Under some regularity conditions, we compute the finite-sample breakdown point and prove the asymptotic normality. Some simulation results and a numerical example are also presented.

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A Robust Wald-Ttype Test in Linear Regression

  • Nam, Ho-Soo
    • Journal of the Korean Statistical Society
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    • 제26권4호
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    • pp.507-520
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    • 1997
  • In this paper we propose a robust Wald-type test which is based on an efficient Mallows-type one-step GM-estimator. The proposed estimator based on the weight function of Song, Park and Nam (1996) has a bounded influence function and a high breakdown point. Under some regularity conditions, we compute the finite-sample breakdown point, and drive asymptotic normality of the proposed estimator. The level and power breakdown points, influence function and asymptotic distribution of the proposed test statistic are main points of this paper. To compare the performance of the proposed test with other tests, we perform some Monte Carlo simulations.

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A Study on a One-step Pairwise GM-estimator in Linear Models

  • Song, Moon-Sup;Kim, Jin-Ho
    • Journal of the Korean Statistical Society
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    • 제26권1호
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    • pp.1-22
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    • 1997
  • In the linear regression model $y_{i}$ = .alpha. $x_{i}$ $^{T}$ .beta. + .epsilon.$_{i}$ , i = 1,2,...,n, the weighted pairwise absolute deviation (WPAD) estimator was defined by minimizing the dispersion function D (.beta.) = .sum..sum.$_{{i $w_{{ij}}$$\mid$ $r_{j}$ (.beta.) $r_{i}$ (.beta.)$\mid$, where $r_{i}$ (.beta.)'s are residuals and $w_{{ij}}$'s are weights. This estimator can achive bounded total influence with positive breakdown by choice of weights $w_{{ij}}$. In this paper, we consider a more general type of dispersion function than that of D(.beta.) and propose a pairwise GM-estimator based on the dispersion function. Under some regularity conditions, the proposed estimator has a bounded influence function, a high breakdown point, and asymptotically a normal distribution. Results of a small-sample Monte Carlo study are also presented. presented.

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A High Breakdown and Efficient GM-Estimator in Linear Models

  • Song, Moon-Sup;Park, Changsoon;Nam, Ho-Soo
    • Journal of the Korean Statistical Society
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    • 제25권4호
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    • pp.471-487
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    • 1996
  • In this paper we propose an efficient scoring type one-step GM-estimator, which has a bounded influence function and a high break-down point. The main point of the estimator is in the weighting scheme of the GM-estimator. The weight function we used depends on both leverage points and residuals So we construct an estimator which does not downweight good leverage points Unider some regularity conditions, we compute the finite-sample breakdown point and prove asymptotic normality Some simulation results are also presented.

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A Robust Estimator in Multivariate Regression Using Least Quartile Difference

  • Jung Kang-Mo
    • Communications for Statistical Applications and Methods
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    • 제12권1호
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    • pp.39-46
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    • 2005
  • We propose an equivariant and robust estimator in multivariate regression model based on the least quartile difference (LQD) estimator in univariate regression. We call this estimator as the multivariate least quartile difference (MLQD) estimator. The MLQD estimator considers correlations among response variables and it can be shown that the proposed estimator has the appropriate equivariance properties defined in multivariate regressions. The MLQD estimator has high breakdown point as does the univariate LQD estimator. We develop an algorithm for MLQD estimate. Simulations are performed to compare the efficiencies of MLQD estimate with coordinatewise LQD estimate and the multivariate least trimmed squares estimate.

An Equivariant and Robust Estimator in Multivariate Regression Based on Least Trimmed Squares

  • Jung, Kang-Mo
    • Communications for Statistical Applications and Methods
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    • 제10권3호
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    • pp.1037-1046
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    • 2003
  • We propose an equivariant and robust estimator in multivariate regression model based on the least trimmed squares (LTS) estimator in univariate regression. We call this estimator as multivariate least trimmed squares (MLTS) estimator. The MLTS estimator considers correlations among response variables and it can be shown that the proposed estimator has the appropriate equivariance properties defined in multivariate regression. The MLTS estimator has high breakdown point as does LTS estimator in univariate case. We develop an algorithm for MLTS estimate. Simulation are performed to compare the efficiencies of MLTS estimate with coordinatewise LTS estimate and a numerical example is given to illustrate the effectiveness of MLTS estimate in multivariate regression.

On Confidence Intervals of High Breakdown Regression Estimators

  • Lee Dong-Hee;Park YouSung;Kim Kang-yong
    • 한국통계학회:학술대회논문집
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    • 한국통계학회 2004년도 학술발표논문집
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    • pp.205-210
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    • 2004
  • A weighted self-tuning robust regression estimator (WSTE) has the high breakdown point for estimating regression parameters such as other well known high breakdown estimators. In this paper, we propose to obtain standard quantities like confidence intervals, and it is found to be superior to the other high breakdown regression estimators when a sample is contaminated

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ROBUST REGRESSION ESTIMATION BASED ON DATA PARTITIONING

  • Lee, Dong-Hee;Park, You-Sung
    • Journal of the Korean Statistical Society
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    • 제36권2호
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    • pp.299-320
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    • 2007
  • We introduce a high breakdown point estimator referred to as data partitioning robust regression estimator (DPR). Since the DPR is obtained by partitioning observations into a finite number of subsets, it has no computational problem unlike the previous robust regression estimators. Empirical and extensive simulation studies show that the DPR is superior to the previous robust estimators. This is much so in large samples.

Self-tuning Robust Regression Estimation

  • Park, You-Sung;Lee, Dong-Hee
    • 한국통계학회:학술대회논문집
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    • 한국통계학회 2003년도 추계 학술발표회 논문집
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    • pp.257-262
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    • 2003
  • We introduce a new robust regression estimator, self-tuning regression estimator. Various robust estimators have been developed with discovery for theories and applications since Huber introduced M-estimator at 1960's. We start by announcing various robust estimators and their properties, including their advantages and disadvantages, and furthermore, new estimator overcomes drawbacks of other robust regression estimators, such as ineffective computation on preserving robustness properties.

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A Study on High Breakdown Discriminant Analysis : A Monte Carlo Simulation

  • Moon Sup;Young Joo;Youngjo
    • Communications for Statistical Applications and Methods
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    • 제7권1호
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    • pp.225-232
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    • 2000
  • The linear and quadratic discrimination functions based on normal theory are widely used to classify an observation to one of predefined groups. But the discriminant functions are sensitive to outliers. A high breakdown procedure to estimate location and scatter of multivariate data is the minimum volume ellipsoid or MVE estimator To obtain high breakdown classifiers outliers in multivariate data are detected by using the robust Mahalanobis distance based on MVE estimators and the weighted estimators are inserted in the functions for classification. A samll-sample MOnte Carlo study shows that the high breakdown robust procedures perform better than the classical classifiers.

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