Efficient dimension reduction for multivariate response data

Yaowu Zhang, Liping Zhu, Yanyuan Ma

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

We propose a semiparametric approach to reduce the covariate dimension for multivariate response data. The method bypasses the conventional inverse regression procedure hence seamlessly avoids the potential difficulties related to the dimension of the response. In addition, coupled with a proper parameterization, the approach allows for statistical inference of the dimension reduction subspace for a wide range of models. The resultant estimator is shown to be root-n consistent, asymptotically normal and semiparametrically efficient. The efficiency gain of the semiparametric approach is significant in both simulations and an application to a primary hypertension study conducted in PR China.

Original languageEnglish (US)
Pages (from-to)187-199
Number of pages13
JournalJournal of Multivariate Analysis
Volume155
DOIs
StatePublished - Mar 1 2017

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Multivariate Response
Dimension Reduction
Parameterization
Dimension-reduction Subspaces
Inverse Regression
Hypertension
Statistical Inference
Covariates
China
Roots
Estimator
Range of data
Simulation
Dimension reduction
Model

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

Cite this

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Efficient dimension reduction for multivariate response data. / Zhang, Yaowu; Zhu, Liping; Ma, Yanyuan.

In: Journal of Multivariate Analysis, Vol. 155, 01.03.2017, p. 187-199.

Research output: Contribution to journalArticle

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