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Optimal Bias in Ridge Regression Approaches To Multicollinearity

Bibliographic Data

ID2331340
AuthorsJohn D Kasarda (University of North Carolina at Chapel Hill), Wen-Fu P Shih (Florida Atlantic Universitv)
Year1977
Volume5
Issue4
Pages461-470
Publication date1977-05-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSociological Methods & Research (JOURNAL)
Journal identifiersISSN: 0049-1241 • E-ISSN: 1552-8294
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/004912417700500405
OpenAlexW2024987078
LanguageEN
Citations received12
References cited11

Ridge regression, based on adding a smally quantity, k, to the diagonal of a correlation matrix of highly collinear independent variables, can reduce the error variance of estimators, but at the expense of introducing bias. Because bias is a monotonic increasing function of k, the problem of the appropriate amount of k to introduce as the ridge analysis increment has yet to be resolved This paper proposes a method for selecting the optimal value of k in terms of minimizing the mean square error of estimation. First, we demonstrate mathematically the existence of a minimum mean square error point of the ridge estimator along the scale k. Second, we present an iterative procedure for locating the k value which will minimize the mean square error of estimates for any correlated data set

Diagonal · Estimator · Mathematical analysis · Mean squared error · Minimum mean square error · Monotonic function · Multicollinearity · Regression · Regression analysis · Ridge · Statistics · Variance (accounting) · Variance inflation factor · Advanced Statistical Methods and Models · Advanced Statistical Process Monitoring · Mathematics · Statistical Methods and Inference

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Unique citing works12
Citations per year0,26
Citation span1979 - 1989 (11)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 10

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