A non stochastic ridge regression estimator and comparison with the James-Stein estimator

This article presents a non-stochastic version of the Generalized Ridge Regression estimator that arises from a discussion of the properties of a Generalized Ridge Regression estimator whose shrinkage parameters are found to be close to their upper bounds. The resulting estimator takes the form of a...

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Bibliographic Details
Published in:Communications in statistics. Theory and methods 2016-04, Vol.45 (8), p.2298-2310
Main Authors: Firinguetti, Luis, Rubio, Hernán, Chaubey, Yogendra P.
Format: Article
Language:English
Subjects:
Economic models
Error analysis
Estimators
James-Stein estimator
Least squares method
Mathematical models
Mean square error
Mean square errors
Multicollinearity
Primary 62J05
Regression
Regression analysis
Ridge regression
Ridges
Secondary 62J07
Shrinkage
Statistics
Citations: Items that this one cites
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Summary:This article presents a non-stochastic version of the Generalized Ridge Regression estimator that arises from a discussion of the properties of a Generalized Ridge Regression estimator whose shrinkage parameters are found to be close to their upper bounds. The resulting estimator takes the form of a shrinkage estimator that is superior to both the Ordinary Least Squares estimator and the James-Stein estimator under certain conditions. A numerical study is provided to investigate the range of signal to noise ratio under which the new estimator dominates the James-Stein estimator with respect to the prediction mean square error.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2013.879892