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A Class of Biased Estimators Based on SVD in Linear Model

A Class of Biased Estimators Based on SVD in Linear Model
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摘要 In this paper, a class of new biased estimators for linear model is proposed by modifying the singular values of the design matrix so as to directly overcome the difficulties caused by ill_conditioning in the design matrix. Some important properties of these new estimators are obtained. By appropriate choices of the biased parameters, we construct many useful and important estimators. An application of these new estimators in three_dimensional position adjustment by distance in a spatial coordiate surveys is given. The results show that the proposed biased estimators can effectively overcome ill_conditioning and their numerical stabilities are preferable to ordinary least square estimation. In this paper, a class of new biased estimators for linear model is proposed by modifying the singular values of the design matrix so as to directly overcome the difficulties caused by ill_conditioning in the design matrix. Some important properties of these new estimators are obtained. By appropriate choices of the biased parameters, we construct many useful and important estimators. An application of these new estimators in three_dimensional position adjustment by distance in a spatial coordiate surveys is given. The results show that the proposed biased estimators can effectively overcome ill_conditioning and their numerical stabilities are preferable to ordinary least square estimation.
出处 《Chinese Quarterly Journal of Mathematics》 CSCD 2003年第1期82-87,共6页 数学季刊(英文版)
基金 SupportedbytheNationalScienceFundofChinaforDistingusiheYoungScholarsofChina(4 0 12 50 13,4 9 82 510 7) SupportedbytheNaturalScienceFoundationofChina(4 0 0 74 0 0 6) SupportedbytheNaturalScienceFoundationofHenanProvince(0 0 4 0 5130 0 )
关键词 有偏估计子 SVD 线性回归模型 奇异值分解 设计矩阵 病态 最小平方估计子 MOORE-PENROSE广义逆 坐标系 ill_conditioning singular value decomposition biased estimation
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