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A Simulation Study of Alternatives to Ordinary Least Squares

A. P. Dempster, Martin Schatzoff and Nanny Wermuth
Journal of the American Statistical Association
Vol. 72, No. 357 (Mar., 1977), pp. 77-91
DOI: 10.2307/2286909
Stable URL: http://www.jstor.org/stable/2286909
Page Count: 15
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A Simulation Study of Alternatives to Ordinary Least Squares
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Abstract

Estimated regression coefficients and errors in these estimates are computed for 160 artificial data sets drawn from 160 normal linear models structured according to factorial designs. Ordinary multiple regression (OREG) is compared with 56 alternatives which pull some or all estimated regression coefficients some or all the way to zero. Substantial improvements over OREG are exhibited when collinearity effects are present, noncentrality in the original model is small, and selected true regression coefficients are small. Ridge regression emerges as an important tool, while a Bayesian extension of variable selection proves valuable when the true regression coefficients vary widely in importance.

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