Dimension reduction estimation for central mean subspace with missing multivariate response

Guo-Liang Fan, Hong-Xia Xu and Han-Ying Liang

Journal of Multivariate Analysis, 2019, vol. 174, issue C

Abstract: Multivariate response data often arise in practice and they are frequently subject to missingness. Under this circumstance, the standard sufficient dimension reduction (SDR) methods cannot be used directly. To reduce the dimension and estimate the central mean subspace, a profile least squares estimation method is proposed based on an inverse probability weighted technique. The profile least squares method does not need any distributional assumptions on the covariates and hence differs from existing SDR methods. The resulting estimator of the central mean subspace is proved to be asymptotically normal and root n consistent under some mild conditions. The structural dimension is determined by a BIC-type criterion and the consistency of its estimator is established. Comprehensive simulations and a real data analysis show that the proposed method works promisingly.

Keywords: Central mean subspace; High dimensionality; Missing data; Multivariate response; Sufficient dimension reduction (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.jmva.2019.104542

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