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Estimation of error variance via ridge regression

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  • X Liu
  • S Zheng
  • X Feng

Abstract

SummaryWe propose a novel estimator of error variance and establish its asymptotic properties based on ridge regression and random matrix theory. The proposed estimator is valid under both low- and high-dimensional models, and performs well not only in nonsparse cases, but also in sparse ones. The finite-sample performance of the proposed method is assessed through an intensive numerical study, which indicates that the method is promising compared with its competitors in many interesting scenarios.

Suggested Citation

  • X Liu & S Zheng & X Feng, 2020. "Estimation of error variance via ridge regression," Biometrika, Biometrika Trust, vol. 107(2), pages 481-488.
    • Handle: RePEc:oup:biomet:v:107:y:2020:i:2:p:481-488.
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    File URL: http://hdl.handle.net/10.1093/biomet/asz074
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    Cited by:

    1. Hu, Jianhua & Liu, Xiaoqian & Liu, Xu & Xia, Ningning, 2022. "Some aspects of response variable selection and estimation in multivariate linear regression," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    2. Xin Wang & Lingchen Kong & Liqun Wang, 2022. "Estimation of Error Variance in Regularized Regression Models via Adaptive Lasso," Mathematics, MDPI, vol. 10(11), pages 1-19, June.
    3. Pierpaolo Angelini, 2024. "Extended Least Squares Making Evident Nonlinear Relationships between Variables: Portfolios of Financial Assets," JRFM, MDPI, vol. 17(8), pages 1-24, August.
    4. Sayanti Guha Majumdar & Anil Rai & Dwijesh Chandra Mishra, 2023. "Estimation of Error Variance in Genomic Selection for Ultrahigh Dimensional Data," Agriculture, MDPI, vol. 13(4), pages 1-16, April.
    5. Choi, Semin & Kim, Yesool & Park, Gunwoong, 2023. "Densely connected sub-Gaussian linear structural equation model learning via ℓ1- and ℓ2-regularized regressions," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).

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