A0455
Title: Modeling and inference for 3D complex objects
Authors: Lily Wang - George Mason University (United States) [presenting]
Guannan Wang - College of William & Mary (United States)
Yueying Wang - Iowa State University (United States)
Abstract: The use of 3D complex objects is growing in various applications as data collection techniques continue to evolve. Identifying and locating significant effects within these objects is essential for making informed decisions based on the data. An advanced nonparametric framework is presented for learning and inferring 3D complex objects, enabling accurate estimation of the underlying signals and efficient detection and localization of significant effects. The proposed method addresses the problem of analyzing 3D complex objects collected within irregular boundaries by modelling them as functional data and utilizing trivariate spline smoothing based on triangulations to estimate the mean functions. In addition, a novel approach is presented for constructing simultaneous confidence corridors to quantify estimation uncertainty, and the procedure is extended to accommodate comparisons between two independent samples. The proposed methods are illustrated through a real-data application using the Alzheimer's Disease Neuroimaging Initiative database.
