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Variable selection through adaptive MAVE

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  • Moradi Rekabdarkolaee, Hossein
  • Wang, Qin

Abstract

Adaptive minimum average variance estimation (MAVE) is an efficient approach for dimension reduction as it can adapt to different error distributions. In this paper, we combine the ideas of adaptive estimation and regression shrinkage, and propose the sparse adaptive MAVE (saMAVE). The saMAVE can estimate the central mean subspace and select informative covariates simultaneously, without assuming any particular model or distribution on the predictor variables. The efficacy of saMAVE is verified through both theoretical results and simulation studies.

Suggested Citation

  • Moradi Rekabdarkolaee, Hossein & Wang, Qin, 2017. "Variable selection through adaptive MAVE," Statistics & Probability Letters, Elsevier, vol. 128(C), pages 44-51.
    • Handle: RePEc:eee:stapro:v:128:y:2017:i:c:p:44-51
    DOI: 10.1016/j.spl.2017.04.012
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