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Title: Statistical learning for modal regression Authors:  Yunlong Feng - The State University of New York at Albany (United States) [presenting]
Jun Fan - Hong Kong Baptist University (Hong Kong)
Johan Suykens - KU Leuven (Belgium)
Abstract: The modal regression problem will be discussed from a statistical learning point of view. It will be shown that modal regression can be approached by means of empirical risk minimization techniques. A framework for analyzing and implementing modal regression within the statistical learning context will be developed. Theoretical results concerning the generalization ability and approximation ability of modal regression estimators will be provided. Connections and differences of the proposed modal regression method with existing ones will also be illustrated. Numerical examples will be given to show the effectiveness of the newly proposed modal regression approach.