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Note on the EM Algorithm in Linear Regression …
International Mathematical Forum, 4, 2009 , no. 38, 1883 - 1889 Note on the EM Algorithm in LinearRegression ModelJi-Xia Wang and Yu MiaoCollege of Mathematics and Information ScienceHenan Normal UniversityHenan Province, 453007, Regression model has been used extensively in the fields ofinformation processing and data analysis. In the present paper, we con-sider the Linear model with missing data. Using the EM (Expectationand Maximization) Algorithm , the asymptotic variances and the stan-dard errors for the MLE of the unknown parameters are Subject Classification:93C05; 93C41 Keywords:Conditional expectation; maximum likelihood estimator; EMalgorithm; Newton-Raphson iteration1 IntroductionAs a typical statistical model , Linear Regression model has been widely used inthe fields of information processing and data analysis. In fact, there have beenseveral statistical methods for its learning or modeling ( , the expectation-maximization (EM) Algorithm [2] for maximum likelihood and the self-organizingnetwork with hyper-ellipsoidal clustering [5]).
International Mathematical Forum, 4, 2009, no. 38, 1883 - 1889 Note on the EM Algorithm in Linear Regression Model Ji-Xia Wang and Yu Miao College of Mathematics and Information Science
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