Spatiotemporal exposure prediction with penalized regression

DOI10.1007/S13253-022-00523-0OpenAlexW4309633820MaRDI QIDQ6050901

Nathan A. Ryder, Joshua P. Keller

Publication date: 12 October 2023

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13253-022-00523-0

zbMATH Keywords

air pollutionshrinkage estimationsiliconparticulate matteruniversal krigingsulfate

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12)

Cites Work

• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Practical large-scale spatio-temporal modeling of particulate matter concentrations
• Spatio-temporal modeling of particulate matter concentration through the SPDE approach
• A spatio-temporal downscaler for output from numerical models
• Nonseparable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with an application to particulate matter analysis
• Statistical Methods for Spatial Data Analysis
• Bayesian geostatistical modelling with informative sampling locations
• A spectral method for spatial downscaling
• Nonseparable, Stationary Covariance Functions for Space–Time Data
• Space‐Time Data fusion Under Error in Computer Model Output: An Application to Modeling Air Quality
• Thin Plate Regression Splines
• Bayesian Spatial Quantile Regression
• Measurement error in two‐stage analyses, with application to air pollution epidemiology

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