EconPapers    
Economics at your fingertips  
 

Gaussian predictive process models for large spatial data sets

Sudipto Banerjee, Alan E. Gelfand, Andrew O. Finley and Huiyan Sang

Journal of the Royal Statistical Society Series B, 2008, vol. 70, issue 4, 825-848

Abstract: With scientific data available at geocoded locations, investigators are increasingly turning to spatial process models for carrying out statistical inference. Over the last decade, hierarchical models implemented through Markov chain Monte Carlo methods have become especially popular for spatial modelling, given their flexibility and power to fit models that would be infeasible with classical methods as well as their avoidance of possibly inappropriate asymptotics. However, fitting hierarchical spatial models often involves expensive matrix decompositions whose computational complexity increases in cubic order with the number of spatial locations, rendering such models infeasible for large spatial data sets. This computational burden is exacerbated in multivariate settings with several spatially dependent response variables. It is also aggravated when data are collected at frequent time points and spatiotemporal process models are used. With regard to this challenge, our contribution is to work with what we call predictive process models for spatial and spatiotemporal data. Every spatial (or spatiotemporal) process induces a predictive process model (in fact, arbitrarily many of them). The latter models project process realizations of the former to a lower dimensional subspace, thereby reducing the computational burden. Hence, we achieve the flexibility to accommodate non-stationary, non-Gaussian, possibly multivariate, possibly spatiotemporal processes in the context of large data sets. We discuss attractive theoretical properties of these predictive processes. We also provide a computational template encompassing these diverse settings. Finally, we illustrate the approach with simulated and real data sets. Copyright (c) 2008 Royal Statistical Society.

Date: 2008
References: Add references at CitEc
Citations View citations in EconPapers (26) Track citations by RSS feed

Downloads: (external link)
http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9868.2008.00663.x link to full text (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssb:v:70:y:2008:i:4:p:825-848

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9868

Access Statistics for this article

Journal of the Royal Statistical Society Series B is currently edited by P. Fryzlewicz and I. Van Keilegom

More articles in Journal of the Royal Statistical Society Series B from Royal Statistical Society Contact information at EDIRC.
Series data maintained by Wiley-Blackwell Digital Licensing ().

 
Page updated 2017-09-29
Handle: RePEc:bla:jorssb:v:70:y:2008:i:4:p:825-848