Spatiotemporal satellite data imputation using sparse functional data analysis

DOI10.1214/21-AOAS1591zbMath1498.62340OpenAlexW4297333620MaRDI QIDQ2080746

Zhengyuan Zhu, Xiongtao Dai, Weicheng Zhu

Publication date: 10 October 2022

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/21-aoas1591

zbMATH Keywords

satelliteimputationsparse functional dataspatiotemporalgap-fill

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Inference from spatial processes (62M30) Functional data analysis (62R10) Applications of statistics in engineering and industry; control charts (62P30)

Uses Software

• R
• geoR
• fda (R)
• foreach
• Rcpp
• microbenchmark
• TIMESAT
• stfit

Cites Work

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• From sparse to dense functional data and beyond
• Uniform convergence rates for nonparametric regression and principal component analysis in functional/longitudinal data
• Smooth optimum kernel estimators of densities, regression curves and modes
• Multi-dimensional functional principal component analysis
• Functional data analysis.
• Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets
• Principal component models for sparse functional data
• A Fused Gaussian Process Model for Very Large Spatial Data
• Components and Completion of Partially Observed Functional Data
• Spatio‐temporal smoothing and EM estimation for massive remote‐sensing data sets
• Space–Time Covariance Functions
• Functional Data Analysis for Sparse Longitudinal Data

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