A Class of Convolution-Based Models for Spatio-Temporal Processes with Non-Separable Covariance Structure

Alexandre Rodrigues, Peter J. Diggle

Research output: Contribution to journalArticle

Abstract

In this article, we propose a new parametric family of models for real-valued spatio-temporal stochastic processes S(x, t) and show how low-rank approximations can be used to overcome the computational problems that arise in fitting the proposed class of models to large datasets. Separable covariance models, in which the spatio-temporal covariance function of S(x, t) factorizes into a product of purely spatial and purely temporal functions, are often used as a convenient working assumption but are too inflexible to cover the range of covariance structures encountered in applications. We define positive and negative non-separability and show that in our proposed family we can capture positive, zero and negative non-separability by varying the value of a single parameter.

Original languageEnglish (US)
Pages (from-to)553-567
Number of pages15
JournalScandinavian Journal of Statistics
Volume37
Issue number4
DOIs
Publication statusPublished - Dec 2010
Externally publishedYes

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Keywords

  • Convolution-based models
  • Non-separability
  • Spatio-temporal processes

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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