Ridge Regression Model
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See: Regression Model, Generalized Linear Regression Model.
References
2009
- http://www.statlets.com/regression_analysis.htm#ridge
- QUOTE: When the predictor variables are highly correlated amongst themselves, the coefficients of the resulting least squares fit may be very imprecise. By allowing a small amount of bias in the estimates, more reasonable coefficients may often be obtained. Ridge regression is one method to address these issues. Often, small amounts of bias lead to dramatic reductions in the variance of the estimated model coefficients.
1970
- (Hoerl & Kennard, 1970) ⇒ Arthur E. Hoerl and Robert W. Kennard. (1970). “Ridge Regression: Biased Estimation for Nonorthogonal Problems.” In: Technometrics, 12(1). http://www.jstor.org/stable/1267351