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Direction Identification and Minimax Estimation by Generalized Eigenvalue Problem in High Dimensional Sparse Regression

Author

Listed:
  • Sauvenier, Mathieu

    (Université catholique de Louvain, LIDAM/CORE, Belgium)

  • Van Bellegem, Sébastien

    (Université catholique de Louvain, LIDAM/CORE, Belgium)

Abstract

In high-dimensional sparse linear regression, the selection and the estimation of the parameters are studied based on an L0−constraint on the direction of the vector of parameters. We first establish a general result for the direction of the vector of parameters, which is identified through the leading generalized eigenspace of measurable matrices. Based on this result, we suggest addressing the best subset selection problem from a new perspective by solving an empirical generalized eigenvalue problem to estimate the direction of the high-dimensional vector of parameters. We then study a new estimator based on the RIFLE algorithm and demonstrate a nonasymptotic bound of the L2 risk, the minimax convergence of the estimator and a central limit theorem. Simulations show the superiority of the proposed inference over some known l0 constrained estimators.

Suggested Citation

  • Sauvenier, Mathieu & Van Bellegem, Sébastien, 2023. "Direction Identification and Minimax Estimation by Generalized Eigenvalue Problem in High Dimensional Sparse Regression," LIDAM Discussion Papers CORE 2023005, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:2023005
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    References listed on IDEAS

    as
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    3. Gold, David & Lederer, Johannes & Tao, Jing, 2020. "Inference for high-dimensional instrumental variables regression," Journal of Econometrics, Elsevier, vol. 217(1), pages 79-111.
    4. Kean Ming Tan & Zhaoran Wang & Han Liu & Tong Zhang, 2018. "Sparse generalized eigenvalue problem: optimal statistical rates via truncated Rayleigh flow," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(5), pages 1057-1086, November.
    5. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    6. Xiaotong Shen & Wei Pan & Yunzhang Zhu & Hui Zhou, 2013. "On constrained and regularized high-dimensional regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(5), pages 807-832, October.
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    Cited by:

    1. Sauvenier, Mathieu & Van Bellegem, Sébastien, 2023. "Goodness-of-fit test in high-dimensional linear sparse models," LIDAM Discussion Papers CORE 2023008, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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    More about this item

    Keywords

    High-dimensional model ; sparsity ; generalized eigenvalue problem ; identification ; best subset selection ; minimax L0 estimation ; central limit theorem;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other

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