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
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.
| 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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