title: Double-smoothing for Varying Coefficient Models.
abstract: Moderation analyses are widely used in biomedical and psychosocial research to investigate differential treatment effects, with moderators frequently identified through testing the significance of the interaction between the predictor and the potential moderator under strong parametric assumptions. Without imposing any parametric forms on how the moderators may affect the relationship between predictors and responses, varying coefficient models address this fundamental problem of strong parametric assumptions with current practice of moderation analysis and provide a much broader class of models for complex moderation relationships. Local polynomial, especially local linear, methods are commonly used in estimating the varying coefficient models. Recently, a double-smoothing (DS) local linear method has been proposed for nonparametric regression models, with nice properties compared to local linear and local cubic methods. In this paper, we generalize DS to varying coefficient models, and show that it holds similar advantages over local linear and local cubic methods.
authors: Department of Biostatistics and Computational Biology, University of Rochester, USA.
authors: Wan
authors: W
authors: Tang
authors: Guoxin
authors: G
authors: Zuo
authors: Hua
authors: H
authors: He