Practical large-scale spatio-temporal modeling of particulate matter concentrations

DOI10.1214/08-AOAS204zbMath1160.62093arXiv0906.1428OpenAlexW2164644006WikidataQ57232635 ScholiaQ57232635MaRDI QIDQ1018627

Jeff D. Yanosky, Robin C. Puett, Helen H. Suh, Francine Laden, Christopher J. Paciorek

Publication date: 20 May 2009

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

Full work available at URL: https://arxiv.org/abs/0906.1428

zbMATH Keywords

smoothingair pollutionepidemiologystochastic EMKrigingadditive modelbackfittinggeoadditive model

Mathematics Subject Classification ID

Related Items (12)

Prediction of particle pollution through spatio-temporal multivariate geostatistical analysis: spatial special issue ⋮ A pseudo-penalized quasi-likelihood approach to the spatial misalignment problem with non-normal data ⋮ Spatiotemporal exposure prediction with penalized regression ⋮ Estimation and inference for exposure effects with latency in the Cox proportional hazards model in the presence of exposure measurement error ⋮ Bayesian modeling of discrete-time point-referenced spatio-temporal data ⋮ Spatio-Temporal Cross-Covariance Functions under the Lagrangian Framework with Multiple Advections ⋮ Spatial models for point and areal data using Markov random fields on a fine grid ⋮ Reduced-rank spatio-temporal modeling of air pollution concentrations in the multi-ethnic study of atherosclerosis and air pollution ⋮ Practical large-scale spatio-temporal modeling of particulate matter concentrations ⋮ A penalized likelihood method for nonseparable space-time generalized additive models ⋮ A class of covariate-dependent spatiotemporal covariance functions for the analysis of daily ozone concentration ⋮ Testing for a changepoint in the Cox survival regression model

Uses Software

• SemiPar
• ElemStatLearn
• gamair

Cites Work

• Unnamed Item
• Unnamed Item
• Practical large-scale spatio-temporal modeling of particulate matter concentrations
• Statistical Methods for Spatial Data Analysis
• Geoadditive Models
• Semiparametric Regression
• Bayesian Spatial Prediction of Random Space-Time Fields With Application to Mapping PM_2.5Exposure
• Nonseparable, Stationary Covariance Functions for Space–Time Data
• Thin Plate Regression Splines
• Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models
• Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models
• The elements of statistical learning. Data mining, inference, and prediction

This page was built for publication: Practical large-scale spatio-temporal modeling of particulate matter concentrations