Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/1165
Title: | Linear least squares regression: a different view |
Authors: | Yatracos, Yannis G. |
Major Field of Science: | Social Sciences |
Keywords: | Regression analysis;Parameter estimation |
Issue Date: | Aug-1996 |
Source: | Statistics and Probability Letters, 1996, vol. 29, no. 2, pp. 143-148 |
Volume: | 29 |
Issue: | 2 |
Start page: | 143 |
End page: | 148 |
Journal: | Statistics & Probability Letters |
Abstract: | The main result of this paper is filling an existing gap between the theory of least squares regression and the solution of linear systems of equations. A linear least squares regression problem with p-parameters over n cases is converted, via non-orthogonal transformations, into a k-parameter regression problem through the origin on n - p + k cases, and p - k equations in diagonal form with p - k unknowns, 0 < k < p. As a consequence of this result: (i) tests and confidence intervals can be easily obtained for any subset of the parameters of the model; (ii) the regression problem can be converted into p-univariate regression problems through the origin based on (n - p + 1) cases only; (iii) one may conclude that we can talk about the influence of the observations on any subset of the least squares estimates; (iv) the PC user may provide solutions to regression problems of higher dimension than the ones previously handled. |
URI: | https://hdl.handle.net/20.500.14279/1165 |
ISSN: | 01677152 |
DOI: | 10.1016/0167-7152(95)00167-0 |
Rights: | © Elsevier |
Type: | Article |
Affiliation : | University of Montreal |
Appears in Collections: | Άρθρα/Articles |
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