Practical large-scale spatio-temporal modeling of particulate matter concentrations
From MaRDI portal
Publication:1018627
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
Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).
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
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 PM2.5Exposure
- 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