Variable selection via RIVAL and its application in nuclear material detection

Er-Wei Bai, P. Kump, K-S. Chan, B. Eichinger, Kang Li

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

In many situations, the number of data points is fixed, and the asymptotic convergence results of popular model selection tools may not be useful. A new algorithm for model selection, RIVAL (removing irrelevant variables amidst Lasso iterations), is presented and shown to be particularly effective for a large but fixed number of data points. The algorithm is motivated by an application of nuclear material detection where all unknown parameters are to be non-negative. Thus, positive Lasso and its variants are analyzed. Then, RIVAL is proposed and is shown to have some desirable properties, namely the number of data points needed to have convergence is smaller than existing methods.
Original languageEnglish
Pages (from-to)2107-2115
Number of pages9
JournalAutomatica
Volume48
Issue number9
DOIs
Publication statusPublished - Sep 2012

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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