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Variable selection under multicollinearity using modified log penalty

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  • Van Cuong Nguyen
  • Chi Tim Ng

Abstract

To handle the multicollinearity issues in the regression analysis, a class of ‘strictly concave penalty function’ is described in this paper. As an example, a new penalty function called ‘modified log penalty’ is introduced. The penalized estimator based on strictly concave penalties enjoys the oracle property under certain regularity conditions discussed in the literature. In the multicollinearity cases where such conditions are not applicable, the behaviors of the strictly concave penalties are discussed through examples involving strongly correlated covariates. Real data examples and simulation studies are provided to show the finite-sample performance of the modified log penalty in terms of prediction error under scenarios exhibiting multicollinearity.

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  • Van Cuong Nguyen & Chi Tim Ng, 2020. "Variable selection under multicollinearity using modified log penalty," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(2), pages 201-230, January.
    • Handle: RePEc:taf:japsta:v:47:y:2020:i:2:p:201-230
    DOI: 10.1080/02664763.2019.1637829
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    Cited by:

    1. Jireh Yi-Le Chan & Steven Mun Hong Leow & Khean Thye Bea & Wai Khuen Cheng & Seuk Wai Phoong & Zeng-Wei Hong & Yen-Lin Chen, 2022. "Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review," Mathematics, MDPI, vol. 10(8), pages 1-17, April.

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