Robust and sparse regression in generalized linear model by stochastic optimization

DOI10.1007/s42081-019-00049-9zbMath1436.62350arXiv1802.03127OpenAlexW2953133993WikidataQ127713541 ScholiaQ127713541MaRDI QIDQ2303494

Takayuki Kawashima, Hironori Fujisawa

Publication date: 4 March 2020

Published in: Japanese Journal of Statistics and Data Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1802.03127

zbMATH Keywords

robustnessdivergencegeneralized linear modelsparsestochastic gradient descent

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Generalized linear models (logistic models) (62J12) Stochastic programming (90C15) Statistical aspects of big data and data science (62R07)

Related Items (2)

Hyperlink regression via Bregman divergence ⋮ Special feature: Information theory and statistics

Uses Software

• AdaGrad

Cites Work

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