AIC for the non-concave penalized likelihood method

DOI10.1007/s10463-018-0649-xzbMath1418.62279arXiv1509.01688OpenAlexW2964033161MaRDI QIDQ2414941

Yusuke Shimizu, Hiroki Masuda, Yuta Umezu, Yoshiyuki Ninomiya

Publication date: 17 May 2019

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1509.01688

zbMATH Keywords

Kullback-Leibler divergencevariable selectioninformation criteriontuning parameterstatistical asymptotic theory\(\ell _q\) regularization

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Ridge regression; shrinkage estimators (Lasso) (62J07) Generalized linear models (logistic models) (62J12)

Related Items (7)

Quasi-likelihood analysis and its applications ⋮ Quasi-likelihood analysis for marked point processes and application to marked Hawkes processes ⋮ Moment convergence of regularized least-squares estimator for linear regression model ⋮ Unnamed Item ⋮ Analyzing order flows in limit order books with ratios of Cox-type intensities ⋮ AIC for the group Lasso in generalized linear models ⋮ Partial quasi-likelihood analysis

Uses Software

• sparsenet

Cites Work

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