Recursive ridge regression using second-order stochastic algorithms

Antoine Godichon-Baggioni, Wei Lu and Bruno Portier

Computational Statistics & Data Analysis, 2024, vol. 190, issue C

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.

Keywords: Ridge regression; Stochastic optimization; Stochastic Newton algorithm; Recursive estimation; Machine learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:190:y:2024:i:c:s0167947323001652

DOI: 10.1016/j.csda.2023.107854

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