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1974•JAWRA Journal of the American Water Resources AssociationRequires access

THE APPLICATION OF RIDGE REGRESSION ANALYSIS TO A HYDROLOGIC TARGET‐CONTROL MODEL1

Donald A. Anderson, Richard G. Scott

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Abstract

ABSTRACT: Ridge regression analysis is used to investigate the stability of regression estimates over twenty‐three years of data in a target‐control model. Two target stations in the Wind River Range in Wyoming are studied using two sets of control variables. The predictive ability of ridge regression analysis is compared to that of ordinary regression analysis. The results of this study indicate improved stability of the estimates of ridge regression over ordinary regression. The predictive ability of the ridge regression estimates is as good or better than regression estimates.

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What this paper is about

ABSTRACT: Ridge regression analysis is used to investigate the stability of regression estimates over twenty‐three years of data in a target‐control model. Two target stations in the Wind River Range in Wyoming are studied using two sets of control variables. The predictive ability of ridge regression analysis is compared to that of ordinary regression analysis. The results of this study indicate improved stability of the estimates of ridge regression over ordinary regression. The predictive ability of the ridge regression estimates is as good or better than regression estimates.

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Available abstract

ABSTRACT: Ridge regression analysis is used to investigate the stability of regression estimates over twenty‐three years of data in a target‐control model. Two target stations in the Wind River Range in Wyoming are studied using two sets of control variables. The predictive ability of ridge regression analysis is compared to that of ordinary regression analysis. The results of this study indicate improved stability of the estimates of ridge regression over ordinary regression. The predictive ability of the ridge regression estimates is as good or better than regression estimates.

Key concepts: Ridge, Regression analysis, Regression, Segmented regression, Regression diagnostic, Linear regression, Statistics, Polynomial regression

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