A Constrained Linear Estimator for Multiple Regression

Autor/inn/en	Davis-Stober, Clintin P.; Dana, Jason; Budescu, David V.
Titel	A Constrained Linear Estimator for Multiple Regression
Quelle	In: Psychometrika, 75 (2010) 3, S.521-541 (21 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-010-9162-8
Schlagwörter	Least Squares Statistics; Multiple Regression Analysis; Heuristics; Tests; Weighted Scores; Psychometrics + Suchen Sie Ihr Suchwort? Heuristik; Examination; Prüfung; Examen; Psychometry; Psychometrie
Abstract	"Improper linear models" (see Dawes, Am. Psychol. 34:571-582, "1979"), such as equal weighting, have garnered interest as alternatives to standard regression models. We analyze the general circumstances under which these models perform well by recasting a class of "improper" linear models as "proper" statistical models with a single predictor. We derive the upper bound on the mean squared error of this estimator and demonstrate that it has less variance than ordinary least squares estimates. We examine common choices of the weighting vector used in the literature, e.g., single variable heuristics and equal weighting, and illustrate their performance in various test cases. (As Provided).
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Update	2017/4/10