Optimal network design for Bayesian spatial prediction of multivariate non-Gaussian environmental data

Firoozeh Rivaz

Journal of Applied Statistics, 2016, vol. 43, issue 7, 1335-1348

Abstract: This paper deals with the problem of increasing air pollution monitoring stations in Tehran city for efficient spatial prediction. As the data are multivariate and skewed, we introduce two multivariate skew models through developing the univariate skew Gaussian random field proposed by Zareifard and Jafari Khaledi [21]. These models provide extensions of the linear model of coregionalization for non-Gaussian data. In the Bayesian framework, the optimal network design is found based on the maximum entropy criterion. A Markov chain Monte Carlo algorithm is developed to implement posterior inference. Finally, the applicability of two proposed models is demonstrated by analyzing an air pollution data set.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2015.1100592 (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:taf:japsta:v:43:y:2016:i:7:p:1335-1348

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2015.1100592

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().