BIASED ESTIMATOR BASED ON DIAGNOSIS AND MEASURE OF MULTICOLLINEARITY
Zhang Lei 1) ;Gu Yongwei 1,2) ;Gui Qingming 1) ; and Ma Chaozhong 1)
1)Institute of Science, Information Engineering University, Zhengzhou 4500012)Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450052
Abstract On the basis of diagnosis and measure of multicollinearity, a new biased estimator of unknown parameters called partial ridge (PR) estimator is proposed for GaussMarkov model. Its properties are discussed, and some important conclusions are drawn. The determination of biased parameter in the PR estimator is discussed too. Both theoretical and computational results demonstrate that the PR estimator is a effective biased estimator for overcoming the effect of multicollinearity and is superior to the ordinary ridge estimator.
Key words ：
multicollinearity
condition index
variance decomposition proportion
signal-to-noise ratio
partial ridge estimator
Received: 01 January 1900
Cite this article:
Zhang Lei ,Gu Yongwei,Gui Qingming et al. BIASED ESTIMATOR BASED ON DIAGNOSIS AND MEASURE OF MULTICOLLINEARITY[J]. , 2007, 27(2): 99-102.
Zhang Lei ,Gu Yongwei,Gui Qingming et al. BIASED ESTIMATOR BASED ON DIAGNOSIS AND MEASURE OF MULTICOLLINEARITY[J]. jgg, 2007, 27(2): 99-102.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2007/V27/I2/99
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