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RISS 인기검색어
- 검색키워드
- 주제어 : LMS and LTS estimators (검색결과 3 건)
- 무료
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- 유료
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A Study on a One-step Pairwise GM-estimator in Linear Models
Song, Moon-Sup,Kim, Jin-Ho The Korean Statistical Society 1997 Journal of the Korean Statistical Society Vol.26 No.1
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.$_{{i<j}}$ $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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On A One-Step GM-Estimator in Linear Models
Nam,Ho Soo 東西大學校 1996 동서논문집 Vol.2 No.1
In this paper we propose an efficient scoring type one-step GM-estimator, which has a bounded influence function and a high breakdown 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. Under some regularity conditions, we compute the finite-sample breaddown point and prove asymptotic normality.
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A High Breakdown and Efficient GM-Estimator in Linear Models
Song, Moon-Sup,Park, Changsoon,Nam, Ho-Soo The Korean Statistical Society 1996 Journal of the Korean Statistical Society Vol.25 No.4
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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