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Activity Number: 305 - New Nonparametric Methods for Functional Data
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #328380
Title: On the Covariance Estimation and Principal Component Analysis for Spatially Dependent Functional Data
Author(s): Haozhe Zhang* and Yehua Li
Companies: Iowa State University and University of California, Riverside
Keywords: functional principal component; spatial dependence; spatio-temporal covariance function; nugget effect; tensor product spline; convergence rate
Abstract:

We consider spatially dependent functional data collected under a geostatistics setting, where locations are sampled from a spatial point process and a random function is observed at each location. The functional response is the sum of a spatially dependent functional effect and a spatially independent functional nugget effect. Observations on each function are made on discrete time points and contaminated with measurement errors. Under the assumption of spatial isotropy, we propose a tensor product spline estimator for the spatio-temporal covariance function. If a coregionalization covariance structure is further assumed, we propose a new functional principal component analysis method that borrow information from neighboring functions. Under a unified framework for both sparse and dense functional data, where the number of observations per curve is allowed to be of any rate relative to the number of functions, we develop the asymptotic convergence rates for the proposed estimators. The proposed methods are illustrated by simulation studies and a motivating example of the home price-rent ratio data in the New York metropolitan area.


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