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  1. Journal of Agricultural, Biological, and Environmental Statistics
  2. Journal of Agricultural, Biological, and Environmental Statistics : Volume 10
  3. Journal of Agricultural, Biological, and Environmental Statistics : Volume 10, Issue 3, September 2005
  4. Bivariate spatial process modeling for constructing indicator or intensity weighted spatial CDFs
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Journal of Agricultural, Biological, and Environmental Statistics : Volume 22
Journal of Agricultural, Biological, and Environmental Statistics : Volume 21
Journal of Agricultural, Biological, and Environmental Statistics : Volume 20
Journal of Agricultural, Biological, and Environmental Statistics : Volume 19
Journal of Agricultural, Biological, and Environmental Statistics : Volume 18
Journal of Agricultural, Biological, and Environmental Statistics : Volume 17
Journal of Agricultural, Biological, and Environmental Statistics : Volume 16
Journal of Agricultural, Biological, and Environmental Statistics : Volume 15
Journal of Agricultural, Biological, and Environmental Statistics : Volume 14
Journal of Agricultural, Biological, and Environmental Statistics : Volume 13
Journal of Agricultural, Biological, and Environmental Statistics : Volume 12
Journal of Agricultural, Biological, and Environmental Statistics : Volume 11
Journal of Agricultural, Biological, and Environmental Statistics : Volume 10
Journal of Agricultural, Biological, and Environmental Statistics : Volume 10, Issue 4, December 2005
Journal of Agricultural, Biological, and Environmental Statistics : Volume 10, Issue 3, September 2005
Bivariate spatial process modeling for constructing indicator or intensity weighted spatial CDFs
Maximum a posteriori estimation of the daily ozone peaks in Mexico City
Mark-recapture with occasion and individual effects: Abundance estimation through Bayesian model selection in a fixed dimensional parameter space
Dating chicks: Calibration and discrimination in a nonlinear multivariate hierarchical growth model
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Using linear-bilinear models for studying gene expression × treatment interaction in microarray experiments
An application of ranked set sampling for mean and median estimation using USDA crop production data
A simulation study on tests of hypotheses and confidence intervals for fixed effects in mixed models for blocked experiments with missing data
Journal of Agricultural, Biological, and Environmental Statistics : Volume 10, Issue 2, June 2005
Journal of Agricultural, Biological, and Environmental Statistics : Volume 10, Issue 1, March 2005
Journal of Agricultural, Biological, and Environmental Statistics : Volume 9
Journal of Agricultural, Biological, and Environmental Statistics : Volume 8
Journal of Agricultural, Biological, and Environmental Statistics : Volume 7
Journal of Agricultural, Biological, and Environmental Statistics : Volume 6

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Bivariate spatial process modeling for constructing indicator or intensity weighted spatial CDFs

Content Provider Springer Nature Link
Author Short, Margaret Carlin, Bradley P. Gelfand, Alan E.
Copyright Year 2005
Abstract A spatial cumulative distribution function (SCDF) gives the proportion of a spatial domain D having the value of some response variable less than a particular level w. This article provides a fully hierarchical approach to SCDF modeling, using a Bayesian framework implemented via Markov chain Monte Carlo (MCMC) methods. The approach generalizes the customary SCDF to accommodate density or indicator weighting. Bivariate spatial processes emerge as a natural approach for framing such a generalization. Indicator weighting leads to conditional SCDFs, useful in studying, for example, adjusted exposure to one pollutant given a specified level of exposure to another. Intensity weighted (or population density weighted) SCDFs are particularly natural in assessments of environmental justice, where it is important to determine if a particular sociodemographic group is being excessively exposed to harmful levels of certain pollutants. MCMC methods (combined with a convenient Kronecker structure) enable straightforward estimates or approximate estimates of bivariate, conditional, and weighted SCDFs. We illustrate our methods with two air pollution datasets, one recording both nitric oxide (NO) and nitrogen dioxide (NO$_{2}$) ambient levels at 67 monitoring sites in central and southern California, and the other concerning ozone exposure and race in Atlanta, GA.
Starting Page 259
Ending Page 275
Page Count 17
File Format PDF
ISSN 10857117
Journal Journal of Agricultural, Biological, and Environmental Statistics
Volume Number 10
Issue Number 3
e-ISSN 15372693
Language English
Publisher Springer-Verlag
Publisher Date 2005-01-01
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Bayesian methods Change of support problem Environmental justice Geographic Information System (GIS) Kriging Markov chain Monte Carlo methods Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics
Content Type Text
Resource Type Article
Subject Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty
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