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A0238
Title: Statistical inference for the mean function of longitudinal imaging data over complicated domains Authors:  Jie Li - School of Statistics, Renmin University of China (China) [presenting]
Abstract: Motivated by longitudinal imaging data, which possesses inherent spatial and temporal correlation, a novel procedure is proposed to estimate its mean function. Functional moving average is applied to depict the dependence among temporally ordered images. Flexible bivariate splines over triangulations are used to handle the irregular domain of images common in imaging studies. The bivariate spline estimator's global and local asymptotic properties for mean function are established with simultaneous confidence corridors (SCCs) as a theoretical byproduct. Under some mild conditions, the proposed estimator and its accompanying SCCs are shown to be consistent and oracle efficient, as if all images were entirely observed without errors. The finite sample performance of the proposed method through Monte Carlo simulation experiments strongly corroborates the asymptotic theory. The proposed method is illustrated by analyzing two seawater potential temperature data sets.