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Sufficient Dimension Folding with Categorical Predictors

In: Festschrift in Honor of R. Dennis Cook

Author

Listed:
  • Yuanwen Wang

    (University of Georgia, Department of Statistics)

  • Yuan Xue

    (University of International Business and Economics, School of Statistics)

  • Qingcong Yuan

    (Miami University, Department of Statistics)

  • Xiangrong Yin

    (University of Kentucky, Dr. Bing Zhang Department of Statistics)

Abstract

In this paper, we study dimension folding for matrix/array structured predictors with categorical variables. The categorical variable information is incorporated into dimension folding for regression and classification. The concepts of marginal, conditional, and partial folding subspaces are introduced, and their connections to central folding subspace are investigated. Three estimation methods are proposed to estimate the desired partial folding subspace. An empirical maximal eigenvalue ratio criterion is used to determine the structural dimensions of the associated partial folding subspace. Effectiveness of the proposed methods is evaluated through simulation studies and an application to a longitudinal data.

Suggested Citation

  • Yuanwen Wang & Yuan Xue & Qingcong Yuan & Xiangrong Yin, 2021. "Sufficient Dimension Folding with Categorical Predictors," Springer Books, in: Efstathia Bura & Bing Li (ed.), Festschrift in Honor of R. Dennis Cook, pages 127-165, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-69009-0_7
    DOI: 10.1007/978-3-030-69009-0_7
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