A Geometric Analysis of When Fixed Weighting Schemes Will Outperform Ordinary Least Squares.

Many researchers have demonstrated that fixed, exogenously chosen weights can be useful alternatives to Ordinary Least Squares (OLS) estimation within the linear model (e.g., Dawes, Am. Psychol. 34:571-582, 1979; Einhorn & Hogarth, Org. Behav. Human Perform. 13:171-192, 1975; Wainer, Psychol. Bull. 83:213-217, 1976). Generalizing the approach of Davis-Stober, Dana, and Budescu (Psychometrika 75:521-541, 2010b), I present an analytic method to determine when a choice of fixed weights will incur less mean squared error than OLS as a function of sample size, error variance, and model predictability. Geometrically, I solve for the region of population β that favors a choice of fixed weights over OLS. I derive closed-form upper and lower bounds on the volume of this region, giving tight bounds on the proportion of population β favoring a choice of fixed weights. I illustrate this methodology with several examples and provide a MATLAB(©) (The MathWorks, Matlab software, version 2009b, 2010) programming implementation of the major results.