Penalized minimum average variance estimation

Tao Wang, Peirong Xu, Lixing ZHU

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

For simultaneous dimension reduction and variable selection for general regression models, including the multi-index model as a special case, we propose a penalized minimum average variance estimation method, combining the ideas of minimum average variance estimation in dimension reduction and regularization in variable selection. The resulting estimator can be found in a computationally efficient manner. Under mild conditions, the new method can consistently select all relevant predictors and has the oracle property. Simulations and a data example demonstrate the effectiveness and efficiency of the proposed method.

Original languageEnglish
Pages (from-to)543-569
Number of pages27
JournalStatistica Sinica
Volume23
Issue number2
DOIs
Publication statusPublished - Apr 2013

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Dimension reduction
  • Minimum average variance estimation
  • Oracle property
  • Single-index model
  • Sufficient dimension reduction
  • Variable selection

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