Least squares theory for possibly singular models

Rao, C. Radhakrishna (1978) Least squares theory for possibly singular models Canadian Journal of Statistics, 6 (1). pp. 19-23. ISSN 0319-5724

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Official URL: http://onlinelibrary.wiley.com/doi/10.2307/3314821...

Related URL: http://dx.doi.org/10.2307/3314821


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.

Item Type:Article
Source:Copyright of this article belongs to Statistical Society of Canada.
Keywords:Gauss-markoff Model; Singular Multivariate Normal; Generalized Inverse; least Squares Theory; Singular Linear Models; Primary 62J05; Secondary 15A09
ID Code:58126
Deposited On:31 Aug 2011 12:31
Last Modified:31 Aug 2011 12:31

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