Ridge Regression as an Alternative to Ordinary Least Squares: Improving Prediction Accuracy and the Interpretation of Beta Weights. Professional File. Number 92, Summer 2004Reportar como inadecuado




Ridge Regression as an Alternative to Ordinary Least Squares: Improving Prediction Accuracy and the Interpretation of Beta Weights. Professional File. Number 92, Summer 2004 - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.



Association for Institutional Research (NJ1)

This article looked at non-experimental data via an ordinary least squares (OLS) model and compared its results to ridge regression models in terms of cross-validation predictor weighting precision when using fixed and random predictor cases and small and large p/n ratio models. A majority of the time with two random predictor cases, ridge regression accuracy was superior to OLS in estimating beta weights. Thus, ridge regression was very useful under this condition. However, when the fixed predictor case was reviewed, OLS was much more precise at estimating predictor weights than the ridge techniques regardless of the p/n ratio. In determining the cross validation accuracy of the ridge estimated weights in respect to the OLS estimated weights, ridge models were superior for improving the accuracy of model prediction. An appendix is included. (Contains 2 tables.)

Descriptors: Regression (Statistics), Prediction, Least Squares Statistics, Computation, Correlation, Educational Research, Administrators, Student Personnel Workers, Higher Education

Association for Institutional Research. 1435 East Piedmont Drive Suite 211, Tallahassee, FL 32308. Tel: 850-385-4155; Fax: 850-383-5180; e-mail: air[at]airweb.org; Web site: http://www.airweb.org





Autor: Walker, David A.

Fuente: https://eric.ed.gov/?q=a&ft=on&ff1=dtySince_1992&pg=6010&id=ED512356







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