AIC for the Lasso in generalized linear models

DOI10.1214/16-EJS1179zbMath1347.62145OpenAlexW2510343895MaRDI QIDQ315399

Shuichi Kawano, Yoshiyuki Ninomiya

Publication date: 21 September 2016

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1473431413

zbMATH Keywords

Kullback-Leibler divergencevariable selectioninformation criteriontuning parameterconvexity lemmastatistical asymptotic theory

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic distribution theory in statistics (62E20) Generalized linear models (logistic models) (62J12)

Related Items (6)

Moment convergence in regularized estimation under multiple and mixed-rates asymptotics ⋮ AIC for the non-concave penalized likelihood method ⋮ Unnamed Item ⋮ Unnamed Item ⋮ AIC for the group Lasso in generalized linear models ⋮ Unnamed Item

Uses Software

• glasso
• covTest

Cites Work

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• Bayesian Measures of Model Complexity and Fit
• Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
• Regularization Parameter Selections via Generalized Information Criterion
• Convex Analysis
• On Information and Sufficiency

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