
- Raw AOD Calibrated AOD MODIS MISR GOES MODIS MISR GOES
- Regression Model Results 4.1 Introduction
- Theoretical Development 2.1 Introduction
- Nonstationary Covariance Functions for Gaussian Process Regression
- 15 NOVEMBER 2004 4343V E N T U R A E T A L . 2004 American Meteorological Society
- Uses of Spatial Modelling in Environmental/Public Health Cluster detection/investigation
- Efficiency and Uncertainty Effects of spatial scale
- Spatial statistics in public health research: methodological opportunities and
- Accounting for space in regression models with binary outcomes
- Methodological Development 3.1 Introduction
- Statistical Exposure Estimation Spatial Confounding Bias
- Spatial bias modeling with application to assessing remotely-sensed
- A Spatial Model Prior Distributions and Starting Values A.1 Prior distributions
- Measurement Error in Spatial Modeling of Environmental Exposures
- Nonstationary Christopher
- Statistical and epidemiological considerations in using remote sensing data for exposure estimation
- A Class of Convolution-based Nonstationary Covariance Functions
- CARNEGIE MELLON UNIVERSITY NONSTATIONARY GAUSSIAN PROCESSES
- Spatial Model Results 5.1 Introduction
- Predicting residential indoor concentrations of nitrogen dioxide, fine particulate matter, and elemental carbon using questionnaire and geographic information system based data
- Conclusions and Future Work 6.1 Summary and Contributions
- CARNEGIE MELLON UNIVERSITY NONSTATIONARY GAUSSIAN PROCESSES
- Nonstationary Covariance Functions for Gaussian Process Regression
- Regression Model Results 4.1 Introduction
- The Annals of Applied Statistics 2009, Vol. 3, No. 1, 370397
- Computational techniques for spatial logistic regression with large Christopher J. Paciorek
- Spatial Modelling Using a New Class of Nonstationary Covariance Functions
- Ecology, 86(6), 2005, pp. 15401547 2005 by the Ecological Society of America
- Technical Vignette 4: When can we ignore temporal correlation in space-time data?
- Technical Vignette 3: Kriging, interpolation, and uncertainty
- Technical Vignette 2: Smoothing characteristics of CAR Christopher Paciorek, Department of Biostatistics, Harvard School of Public Health
- Introduction Statistical Models
- Risk Factor Country-region
- Statistical Framework Spatial Representations and Computations
- Introduction Statistical Framework
- Efficiency and Uncertainty The importance of scale
- Associations of MODIS AOD retrievals and PM2.5 Raw MODIS AOD Calibrated MODIS AOD
- Introduction Statistical Framework
- Enhancing Exposure Assessment Using Exposure Predictions in Health Models
- Introduction Model assessment and results
- The effect of spatial scale in regression models with spatial confounding
- Introduction Calibrating MISR AOD
- INTEGRATING SATELLITE AND MONITORING DATA TO RETROSPECTIVELY ESTIMATE MONTHLY PM2.5 CONCENTRATIONS IN THE EASTERN U.S.
- Statistics Useful for Deterministic Models: Evaluation, Calibration, Extension, Integration,
- Post-glacial vegetation dynamics: Bayesian inference for forest composition
- Post-glacial vegetation dynamics: Bayesian inference for tree abundances
- Gaussian processes for spatial modelling in environmental health
- Accounting for space in regression models with binary outcomes
- Spatial smoothing using Gaussian processes Chris Paciorek
- Nonstationary Covariance Functions for Spatial Modelling
- Nonparametric Regression Using Nonstationary Gaussian Processes
- Introduction 1.1 Problem Definition
- Conclusions and Future Work 6.1 Summary and Contributions
- Nonstationary Covariance Functions for Gaussian Process Regression
- Technical Vignette 1: Why kriging in ArcGIS may be a bad idea: A statistician's perspective
- A Spatial Model Prior Distributions and Starting Values A.1 Prior distributions
- Challenges in Integrating Remote Sensing and Ground Monitoring Data When Estimating PM2.5
- CARNEGIE MELLON UNIVERSITY NONSTATIONARY GAUSSIAN PROCESSES
- Background and model structure Analytic comparison of MRF specifications
- Efficiency and Uncertainty The importance of scale
- Mapping Ancient Forests: Bayesian Inference for Spatio-Temporal Trends in Forest Composition
- Nonstationary Gaussian Processes for Regression and Spatial Modelling
- CARNEGIE MELLON UNIVERSITY NONSTATIONARY GAUSSIAN PROCESSES
- Methodological Development 3.1 Introduction
- Theoretical Development 2.1 Introduction
- 1 JULY 2002 1573P A C I O R E K E T A L . 2002 American Meteorological Society
- Introduction Monthly Analyses
- Nonstationary Covariance Functions for Spatial Modelling
- Introduction 1.1 Problem Definition
- Introduction Statistical Framework
- Technical Vignette 5: Understanding intrinsic Gaussian Markov random field spatial models, including intrinsic
- Statistical integration of disparate information for spatially-resolved PM exposure estimation
- Spatial Model Results 5.1 Introduction
- Statistical Science 2010, Vol. 25, No. 1, 107125
- The demographics of resprouting in tree and shrub species of a moist tropical forest
- Technical Vignette 6: Solving systems of equations, generating multivariate normal draws, and inverting