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

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  • Luis Firinguetti
  • Hernán Rubio
  • Yogendra P. Chaubey

Abstract

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.

Suggested Citation

  • Luis Firinguetti & Hernán Rubio & Yogendra P. Chaubey, 2016. "A non stochastic ridge regression estimator and comparison with the James-Stein estimator," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(8), pages 2298-2310, April.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:8:p:2298-2310
    DOI: 10.1080/03610926.2013.879892
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