Estimation on principal component of multi-collinearity Gauss-Markov model based on minimum description length
(Shandong University of Technology, Zibo 255049, China)
Abstract: 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.
Key words: minimum description length； Gauss-Markov model； multi-collinearity； principal component estimation