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

ABSTRACT: We consider spatially dependent functional data collected under a geostatistics setting, where locations are sampled from a spatial point process. 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 covarian

SUBMITTER: Zhang H 

PROVIDER: S-EPMC9793858 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature