Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Accounting for covariate information in the scale component of spatio-temporal mixing models

Tools
- Tools
+ Tools

Bueno, Renata S., Fonseca, Thais Cristina Oliveira and Schmidt, Alexandra M. (2017) Accounting for covariate information in the scale component of spatio-temporal mixing models. Spatial Statistics, 22 (Pt 1). pp. 196-218. doi:10.1016/j.spasta.2017.09.003

[img]
Preview
PDF
WRAP-accounting-covariate-scale-spatio-temporal-models-Fonseca-2017.pdf - Accepted Version - Requires a PDF viewer.
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.

Download (1206Kb) | Preview
Official URL: https://doi.org/10.1016/j.spasta.2017.09.003

Request Changes to record.

Abstract

Spatio-temporal processes in the environmental science are usually assumed to follow a Gaussian process, possibly after some transformation. Gaussian processes might not be appropriate to handle the presence of outlying observations. Our proposal is based on the idea of modelling the process as a scale mixture between a Gaussian and log-Gaussian process. And the novelty is to allow the scale process to vary as a function of covariates. The resultant model has a nonstationary covariance structure in space. Moreover, the resultant kurtosis varies with location, allowing the time series at each location to have different distributions with different tail behaviour. Inference procedure is performed under the Bayesian framework. The analysis of an artificial dataset illustrates how this proposal is able to capture heterogeneity in space caused by dependence on some spatial covariate or by a transformation of the process of interest. Furthermore, an application to maximum temperature data observed in the Spanish Basque country illustrates the effects of altitude in the variability of the process and how our proposed model identifies this dependence through parameters which can be interpreted as regression coefficients in the variance model.

Item Type: Journal Article
Subjects: H Social Sciences > HA Statistics
Divisions: Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH): Bayesian statistical decision theory, Spatial analysis (Statistics), Gaussian processes, Environmental sciences -- Statistical methods
Journal or Publication Title: Spatial Statistics
Publisher: Elsevier B.V.
ISSN: 2211-6753
Official Date: November 2017
Dates:
DateEvent
November 2017Published
18 October 2017Available
26 September 2017Accepted
Volume: 22
Number: Pt 1
Page Range: pp. 196-218
DOI: 10.1016/j.spasta.2017.09.003
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Related URLs:
  • Publisher

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us