Sparse estimation of generalized linear models (GLM) via approximated information criteria

Xiaogang Su, Juanjuan Fan, Richard A. Levine, Martha E. Nunn, Chih Ling Tsai

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

We propose a sparse estimation method, termed MIC (Minimum approximated Information Criterion), for generalized linear models (GLM) in fixed dimensions. What is essentially involved in MIC is the approximation of the 0-norm by a continuous unit dent function. A reparameterization step is devised to enforce sparsity in parameter estimates while maintaining the smoothness of the objective function. MIC yields superior performance in sparse estimation by optimizing the approximated information criterion without reducing the search space and is computationally advantageous since no selection of tuning parameters is required. Moreover, the reparameterization tactic leads to valid significance testing results free of post-selection inference. We explore the asymptotic properties of MIC, and illustrate its usage with simulated experiments and empirical examples.

Original languageEnglish (US)
Pages (from-to)1561-1581
Number of pages21
JournalStatistica Sinica
Volume28
Issue number3
DOIs
StatePublished - Jul 2018

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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