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Comments on: \(\ell _{1}\)-penalization for mixture regression models

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Publication:619144
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DOI10.1007/s11749-010-0200-8zbMath1203.62126OpenAlexW1990064192MaRDI QIDQ619144

Jinchi Lv, Jianqing Fan

Publication date: 22 January 2011

Published in: Test (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11749-010-0200-8



Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Generalized linear models (logistic models) (62J12)




Cites Work

  • Nearly unbiased variable selection under minimax concave penalty
  • A unified approach to model selection and sparse recovery using regularized least squares
  • The Adaptive Lasso and Its Oracle Properties
  • One-step sparse estimates in nonconcave penalized likelihood models
  • Persistene in high-dimensional linear predictor-selection and the virtue of overparametrization
  • Variable selection using MM algorithms
  • A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
  • Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
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