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Activity Number: 253
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Imaging
Abstract #320518 View Presentation
Title: Nonparametric Estimation and Classification for Functional Data with Spatial Correlation
Author(s): Yuan Wang* and Brian Hobbs and Kim-Anh Do and Jianhua Hu
Companies: Washington State University and MD Anderson Cancer Center and MD Anderson Cancer Center and MD Anderson Cancer Center
Keywords: kernel smoothing ; supervised classification ; CT pefusion imaging

Functional data are often generated by modern biomedical technologies where features related to the pathophysiology and pathogenesis of a disease are interrogated repeatedly over time and at multiple spatially interdependent units. To reduce model complexity and simplify the resulting inference, possible spatial correlation among neighboring units is often neglected. In this article, we propose a weighted kernel smoothing estimate of the mean function that leverages the spatial and temporal correlation. We also address the companion problem of developing a simultaneous prediction method for individual curves using discrete samples. We establish the asymptotic properties of the proposed estimate, including its unique maximum efficiency achieving minimum asymptotic variance. We further develop a likelihood based classification using multivariate functional data with spatial correlation incorporated. The proposed methods are applied to discriminate between regions of liver that contain pathologically verified metastases from normal liver tissue.

Authors who are presenting talks have a * after their name.

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