Journal article
Nonparametric Bayesian models for a spatial covariance
Statistical Methodology, Vol.9(1), pp.265-274
2012
DOI: 10.1016/j.stamet.2011.01.007
PMCID: PMC3743268
PMID: 23956705
Abstract
A crucial step in the analysis of spatial data is to estimate the spatial correlation function that determines the relationship between a spatial process at two locations. The standard approach to selecting the appropriate correlation function is to use prior knowledge or exploratory analysis, such as a variogram analysis, to select the correct parametric correlation function. Rather that selecting a particular parametric correlation function, we treat the covariance function as an unknown function to be estimated from the data. We propose a flexible prior for the correlation function to provide robustness to the choice of correlation function. We specify the prior for the correlation function using spectral methods and the Dirichlet process prior, which is a common prior for an unknown distribution function. Our model does not require Gaussian data or spatial locations on a regular grid. The approach is demonstrated using a simulation study as well as an analysis of California air pollution data.
Details
- Title: Subtitle
- Nonparametric Bayesian models for a spatial covariance
- Creators
- Brian J ReichMontserrat Fuentes - North Carolina State University
- Resource Type
- Journal article
- Publication Details
- Statistical Methodology, Vol.9(1), pp.265-274
- DOI
- 10.1016/j.stamet.2011.01.007
- PMID
- 23956705
- PMCID
- PMC3743268
- NLM abbreviation
- Stat Methodol
- ISSN
- 1572-3127
- eISSN
- 1878-0954
- Publisher
- Elsevier B.V
- Language
- English
- Date published
- 2012
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Provost Office Administration
- Record Identifier
- 9983764492702771
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