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Recursive ridge regression using second-order stochastic algorithms

Author

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  • Godichon-Baggioni, Antoine
  • Lu, Wei
  • Portier, Bruno

Abstract

Recursive second-order stochastic algorithms are presented for solving ridge regression problems in the linear and binary logistic case. The proposed algorithms allow to update the estimates of ridge solution when the data arrive in continuous flow. Some guarantees on the almost sure behavior of the proposed algorithms are established. Numerical experiments on simulated and real-world data show the advantages of our algorithms compared to alternative methods.

Suggested Citation

  • Godichon-Baggioni, Antoine & Lu, Wei & Portier, Bruno, 2024. "Recursive ridge regression using second-order stochastic algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:csdana:v:190:y:2024:i:c:s0167947323001652
    DOI: 10.1016/j.csda.2023.107854
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    References listed on IDEAS

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    1. Gadat, Sébastien & Bercu, Bernard & Bigot, Jérémie & Siviero, Emilia, 2021. "A Stochastic Gauss-Newton Algorithm for Regularized Semi-discrete Optimal Transport," TSE Working Papers 21.1231, Toulouse School of Economics (TSE).
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    3. Volodya Vovk, 2001. "Competitive On‐line Statistics," International Statistical Review, International Statistical Institute, vol. 69(2), pages 213-248, August.
    4. S. le Cessie & J. C. van Houwelingen, 1992. "Ridge Estimators in Logistic Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(1), pages 191-201, March.
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