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Weighted Ridge Regression: Combining Ridge and Robust Regression Methods

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  • Paul W. Holland

Abstract

This paper gives the formulas for and derivation of ridge regression methods when there are weights associated with each observation. A Bayesian motivation is used and various choices of k are discussed. A suggestion is made as to how to combine ridge regression with robust regression methods.

Suggested Citation

  • Paul W. Holland, 1973. "Weighted Ridge Regression: Combining Ridge and Robust Regression Methods," NBER Working Papers 0011, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:0011
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    File URL: http://www.nber.org/papers/w0011.pdf
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    Cited by:

    1. David C. Hoaglin, 1973. "Monte Carlo Techniques in Studying Robust Estimators," NBER Working Papers 0016, National Bureau of Economic Research, Inc.
    2. Jinse Jacob & R Varadharajan, 2024. "Robust Variance Inflation Factor: A Promising Approach for Collinearity Diagnostics in the Presence of Outliers," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(2), pages 845-871, November.
    3. G.S. Maddala, 1974. "Ridge Estimators for Distributed Lag Models," NBER Working Papers 0069, National Bureau of Economic Research, Inc.
    4. Asuman Turkmen & Omer Ozturk, 2014. "Rank-based ridge estimation in multiple linear regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(4), pages 737-754, December.

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