Least squares theory for possibly singular models
C. Radhakrishna Rao · Canadian Journal of Statistics · 1978
Abstract In a recent paper, Scobey (1975) observed that the usual least squares theory can be applied even when the covariance matrix σ2V of Y in the linear model Y = Xβ + e is singular by choosing the Moore‐Penrose inverse (V+XX′)+ instead of V‐1 when V is nonsingular. This result appears to be wrong. The appropriate treatment of the problem in the singular case is described.