A Theorem on Least Squares in Multivariate Linear Regression
Journal of the American Statistical AssociationPublished 1 December 1967
C. G. Khatri
Citations4
SJR quartileQ1
SJR score4.10
SNIP3.08
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Abstract
Abstract This note shows that the least squares estimate of the matrix of coefficients in multivariate linear regression model maximizes all the sample canonical correlations between the dependent variables and the linear transformations of the explanatory variables, and it maximizes all the characteristic roots of the sample regression sum of squares and products matrix due to regression of the dependent variables on the linear transformations of the explanatory variables.
Keywords
Computer Science
Journal of the American Statistical AssociationA Theorem on Least Squares and Vector Correlation in Multivariate Linear Regression
8 Citations1966Gregory C. Chow