Multiple Regression with a Singular Matrix

M. J. R. Healy

Journal of the Royal Statistical Society Series C, 1968, vol. 17, issue 2, 110-117

Abstract: In certain applications of multiple regression the coefficient matrix of the normal equations is singular and therefore does not possess an inverse. Recent theoretical developments make it possible to broaden the theory of least squares so as to include this situation without having to treat it as a special case, and these can be paralleled by appropriate numerical techniques. This paper gives a survey of these developments and outlines some of their possible applications.

Date: 1968
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