Approximate penalization path for smoothly clipped absolute deviation

Research output: Contribution to journalArticlepeer-review

Abstract

Feature selection often constitutes one of the central aspects of many scientific investigations. Among the methodologies for feature selection in penalized regression, the smoothly clipped and absolute deviation seems to be very useful because it satisfies the oracle property. However, its estimation algorithms such as the local quadratic approximation and the concave-convex procedure are not computationally efficient. In this paper, we propose an efficient penalization path algorithm. Through numerical examples on real and simulated data, we illustrate that our path algorithm can be useful for feature selection in regression problems.

Original languageEnglish
Pages (from-to)643-652
Number of pages10
JournalJournal of Statistical Computation and Simulation
Volume82
Issue number5
DOIs
StatePublished - May 2012

Keywords

  • feature selection
  • penalization path
  • penalized regression
  • smoothly clipped and absolute deviation

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