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Estimation of Error Variance in Regularized Regression Models via Adaptive Lasso

Author

Listed:
  • Xin Wang

    (Department of Applied Mathematics, Beijing Jiaotong University, Beijing 100044, China)

  • Lingchen Kong

    (Department of Applied Mathematics, Beijing Jiaotong University, Beijing 100044, China)

  • Liqun Wang

    (Department of Statistics, University of Manitoba, Winnipeg, MB R3T 2N2, Canada)

Abstract

Estimation of error variance in a regression model is a fundamental problem in statistical modeling and inference. In high-dimensional linear models, variance estimation is a difficult problem, due to the issue of model selection. In this paper, we propose a novel approach for variance estimation that combines the reparameterization technique and the adaptive lasso, which is called the natural adaptive lasso. This method can, simultaneously, select and estimate the regression and variance parameters. Moreover, we show that the natural adaptive lasso, for regression parameters, is equivalent to the adaptive lasso. We establish the asymptotic properties of the natural adaptive lasso, for regression parameters, and derive the mean squared error bound for the variance estimator. Our theoretical results show that under appropriate regularity conditions, the natural adaptive lasso for error variance is closer to the so-called oracle estimator than some other existing methods. Finally, Monte Carlo simulations are presented, to demonstrate the superiority of the proposed method.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:11:p:1937-:d:832225
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    References listed on IDEAS

    as
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