Sufficient Dimension Folding with Categorical Predictors

Yuanwen Wang (), Yuan Xue (), Qingcong Yuan () and Xiangrong Yin ()
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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

A chapter in Festschrift in Honor of R. Dennis Cook, 2021, pp 127-165 from Springer

Abstract: 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.

Keywords: Central folding subspace; Partial folding subspace; Sufficient dimension folding (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-69009-0_7

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DOI: 10.1007/978-3-030-69009-0_7

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