Robust combination of model selection methods for prediction

Xiaoqiao Wei, Yuhong Yang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

One important goal of regression analysis is prediction. In recent years, the idea of combining different statistical methods has attracted an increasing attention. In this work, we propose a method, l1-ARM (adaptive regression by mixing), to robustly combine model selection methods that performs well adaptively. In numerical work, we consider the LASSO, SCAD, and adaptive LASSO in representative scenarios, as well as in cases of randomly generated models. The l1-ARM automatically performs like the best among them and consequently provides a better estimation/prediction in an overall sense, especially when outliers are likely to occur.

Original languageEnglish (US)
Pages (from-to)1021-1040
Number of pages20
JournalStatistica Sinica
Volume22
Issue number3
DOIs
StatePublished - Jul 1 2012

Keywords

  • ARM
  • Adaptive LASSO
  • Combining model selection methods
  • LASSO
  • SCAD

Fingerprint Dive into the research topics of 'Robust combination of model selection methods for prediction'. Together they form a unique fingerprint.

Cite this