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Multiple regression models, variable selection problem

Author: ZuoChuang
Tutor: MaWeiJun
School: Heilongjiang University
Course: Applied Mathematics
Keywords: Regression analysis Variable selection AIC criterion BIC rule Fisher information Kullback-Leiber
CLC: O212.1
Type: Master's thesis
Year: 2011
Downloads: 98
Quote: 0
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Abstract


Regression analysis is a multivariate statistical analysis of the most widely used in the various methods , a method of analysis , whose purpose is the study and treatment of the interdependencies between multiple variables . Regression analysis is a very important process to create a good fit regression model based on known data , and the establishment of a regression model is how to select from a large number of explanatory variables which important variables that variable selection problem . This paper is divided into five parts of variable selection issues . First, starting from the multiple regression analysis , and brief multiple regression model . Second, the issue of multiple regression , a common method for variable selection . For variable selection method to introduce and study several important variables selection criteria . Fourth, do a brief comparison of some properties of the guidelines . Fifth , in the previous study and compared on the basis of several criteria , the Fisher information and Kullback-Leiber information , certain improvements the AIC criterion . And stochastic simulation data to verify its applicability .

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CLC: > Mathematical sciences and chemical > Mathematics > Probability Theory and Mathematical Statistics > Mathematical Statistics > General mathematical statistics
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