Feature filter for estimating central mean subspace and its sparse solution

Pei Wang, Xiangrong Yin, Qingcong Yuan, Richard Kryscio

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Sufficient dimension reduction, replacing the original predictors with a few linear combinations while keeping all the regression information, has been widely studied. A key goal is to find the central mean subspace, the intersection of all subspaces that provide such a reduction. To this end, a new sufficient dimension reduction method is proposed, with two estimation procedures, through a novel approach of feature filter, applicable to both univariate and multivariate responses. Asymptotic results are established. Estimation methods to determine the structural dimension, to obtain sparse estimator and to deal with large p small n data are provided. The efficacy of the method is demonstrated by simulations and a real data example.

Original languageEnglish
Article number107285
JournalComputational Statistics and Data Analysis
StatePublished - Nov 2021

Bibliographical note

Funding Information:
This research is partially supported by an NSF grant CIF-1813330 .

Publisher Copyright:
© 2021 Elsevier B.V.


  • Central mean subspace
  • Characteristic function
  • Feature filter
  • Sufficient dimension reduction

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics


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