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A structured covariance ensemble for sufficient dimension reduction

Author

Listed:
  • Qin Wang

    (The University of Alabama)

  • Yuan Xue

    (University of International Business and Economics)

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.

Suggested Citation

  • Qin Wang & Yuan Xue, 2023. "A structured covariance ensemble for sufficient dimension reduction," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(3), pages 777-800, September.
    • Handle: RePEc:spr:advdac:v:17:y:2023:i:3:d:10.1007_s11634-022-00524-4
    DOI: 10.1007/s11634-022-00524-4
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    References listed on IDEAS

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