Estimation on principal component of multi-collinearity Gauss-Markov model based on minimum description length  

作　　者：SHI Yu-feng~(1, 2) (1. Shandong University of Technology, Zibo 255049, China 2. Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan 430079, China) 

出　　处：《中国有色金属学会会刊：英文版》2005年第S1期153-155,共3页Transactions of Nonferrous Metals Society of China

基　　金：Project(40074001) supported by National Natural Science Foundation of China ; Project (SD2003 -10) supported by the Open ResearchFund Programof the Key Laboratory of Geomatics and Digital Technilogy ,Shandong Province

摘　　要：Gauss-Markov model is frequently used in data analysis; the analysis and estimation of its parameters is always a hot issue. Based on the information theory and from the viewpoint of optimal information on description—minimum description length, this paper discusses a case: where there is multi-collinearity in the coefficient matrix, principal component estimation is used to estimate and select the original parameters, so as to reduce its multi-collinearity and improve its credibility. From the viewpoint of minimum description length, this paper discusses the approach of selecting principal components and uses this approach to solve a practical problem.Gauss-Markov model is frequently used in data analysis; the analysis and estimation of its parameters is always a hot issue. Based on the information theory and from the viewpoint of optimal information on description—minimum description length, this paper discusses a case: where there is multi-collinearity in the coefficient matrix, principal component estimation is used to estimate and select the original parameters, so as to reduce its multi-collinearity and improve its credibility. From the viewpoint of minimum description length, this paper discusses the approach of selecting principal components and uses this approach to solve a practical problem.

关 键 词：minimum DESCRIPTION LENGTH Gauss-Markov MODEL multi-collinearity principal COMPONENT ESTIMATION 

分 类 号：O236[理学—运筹学与控制论]