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Title: A sufficient dimension reduction method via expectation of conditional difference Authors:  Qingcong Yuan - Miami University (United States) [presenting]
Abstract: An approach is introduced to sufficient dimension reduction problems using an expectation of conditional difference measure. The proposed method requires very mild conditions on the predictors, estimates the central subspace effectively and is especially useful when the response is categorical. It keeps the model-free advantage without estimating link function. Under regularity conditions, root-n consistency and asymptotic normality are established. The proposed method is very competitive and robust comparing to existing dimension reduction methods through simulations results.