On estimation efficiency of the central mean subspace

Yanyuan Ma, Liping Zhu

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Summary: We investigate the estimation efficiency of the central mean subspace in the framework of sufficient dimension reduction. We derive the semiparametric efficient score and study its practical applicability. Despite the difficulty caused by the potential high dimension issue in the variance component, we show that locally efficient estimators can be constructed in practice. We conduct simulation studies and a real data analysis to demonstrate the finite sample performance and gain in efficiency of the proposed estimators in comparison with several existing methods.

Original languageEnglish (US)
Pages (from-to)885-901
Number of pages17
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume76
Issue number5
DOIs
StatePublished - Nov 1 2014

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Subspace
Sufficient Dimension Reduction
Efficient Estimator
Variance Components
Higher Dimensions
Data analysis
Simulation Study
Estimator
Demonstrate
Framework
Simulation study
Dimension reduction
Finite sample
Variance components

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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On estimation efficiency of the central mean subspace. / Ma, Yanyuan; Zhu, Liping.

In: Journal of the Royal Statistical Society. Series B: Statistical Methodology, Vol. 76, No. 5, 01.11.2014, p. 885-901.

Research output: Contribution to journalArticle

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AB - Summary: We investigate the estimation efficiency of the central mean subspace in the framework of sufficient dimension reduction. We derive the semiparametric efficient score and study its practical applicability. Despite the difficulty caused by the potential high dimension issue in the variance component, we show that locally efficient estimators can be constructed in practice. We conduct simulation studies and a real data analysis to demonstrate the finite sample performance and gain in efficiency of the proposed estimators in comparison with several existing methods.

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