Thursday, September 24, 2020 — 4:00 PM EDT

Please Note: This seminar will be given online.

Statistics and Biostatistics Seminar Series

Yehua Li
University of California, Riverside

Link to join seminar: Hosted on Zoom.

Meeting ID: 844 283 6948 

Passcode: 318995

Unified Principal Component Analysis for Sparse and Dense Functional Data under Spatial Dependency


We consider spatially dependent functional data collected under a geostatistics setting, where spatial locations are irregular and random. 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 stationarity and isotropy, we propose a tensor product spline estimator for the spatio-temporal covariance function. When a coregionalization covariance structure is further assumed, we propose a new functional principal component analysis method that borrows information from neighboring functions. The proposed method also generates nonparametric estimators for the spatial covariance functions, which can be used for functional kriging. Under a unified framework for sparse and dense functional data, infill and increasing domain asymptotic paradigms, we develop the asymptotic convergence rates for the proposed estimators. Advantages of the proposed approach are demonstrated through simulation studies and two real data applications representing sparse
and dense functional data, respectively.

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