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Activity Number: 152 - Recent Development in Sufficient Dimension Reduction
Type: Topic Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: International Statistical Institute
Abstract #322635
Title: On Sufficient Dimension Reduction with Missing Responses Through Estimating Equations
Author(s): Yuexiao Dong* and Qi Xia and Chengyong Tang
Companies: Temple University and Temple University and Temple University
Keywords: Complete-case analysis ; Inverse probability weighting ; Kernel inverse regression ; Linear conditional mean ; Missing at random
Abstract:

A linearity condition is required for all the existing sufficient dimension reduction methods that deal with missing data. Since Li and Dong (2009) and Ma and Zhu (2012), several procedures based on estimating equations have been proposed to remove the linearity condition for sufficient dimension reduction without missing data. In this paper, we propose two new estimating equation procedures to handle missing response in sufficient dimension reduction: the complete-case estimating equation approach and the inverse probability weighted estimating equation approach. The consistency of the estimators are established, and their superb finite sample performances are demonstrated through extensive numerical studies as well as a real data analysis of the New Zealand mussel data.


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