Title: Sufficient dimension folding with categorical predictors
Authors: Yuanwen Wang - University of Georgia (United States)
Yuan Xue - University of International Business and Economics (China)
Qingcong Yuan - Miami University (United States) [presenting]
Xiangrong Yin - University of Kentucky (United States)
Abstract: Dimension folding is studied 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 the central folding subspaces are investigated. 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. The effectiveness of the proposed methods is evaluated through simulation studies and an application to longitudinal data.