A structured covariance ensemble for sufficient dimension reduction

Qin Wang () and Yuan Xue ()
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Qin Wang: The University of Alabama
Yuan Xue: University of International Business and Economics

Advances in Data Analysis and Classification, 2023, vol. 17, issue 3, No 9, 777-800

Abstract: Abstract Sufficient dimension reduction (SDR) is a useful tool for high-dimensional data analysis. SDR aims at reducing the data dimensionality without loss of regression information between the response and its high-dimensional predictors. Many existing SDR methods are designed for the data with continuous responses. Motivated by a recent work on aggregate dimension reduction (Wang in Stat Si 30:1027–1048, 2020), we propose a unified SDR framework for both continuous and binary responses through a structured covariance ensemble. The connection with existing approaches is discussed in details and an efficient algorithm is proposed. Numerical examples and a real data application demonstrate its satisfactory performance.

Keywords: Aggregate dimension reduction; Central subspace; Ensemble learning; Ordinary least squares; Sufficient dimension reduction; 62Hxx; Multivariate; analysis (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11634-022-00524-4

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