Model Averaging of Multivariate Linear Regression Models with Heteroscedastic Covariance Matrix
The average method of statistical model is a hot issue in the field of statistics research,which can effectively improve the accuracy of statistical predic-tion.In statistics,multivariate linear regression model is a kind of important and practical linear statistical model.This paper mainly studies the average method of this kind of model when the random error matrix is not completely equal.We find a matrix to"unify"the different covariance matrices of each line,and then obtain the corresponding Mahalanobis CV weight selection criteria based on Mahalanobis dis-tance by cross-validation method,and prove the asymptotic optimality of the average estimation of the corresponding model.Simulation results show that the new method is better than S-AIC,S-BIC,MMA and JMA of linear regression model with single dependent variable and MMMA of multivariate linear regression model in general.
Multivariate linear regression modelheteroscedastic covariance matrixMCV criterionasymptotic optimality
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