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A class of convolution-based models for spatio-temporal processes with non-separable covariance structure

Rodrigues, Alexandre and Diggle, Peter J. (2010) A class of convolution-based models for spatio-temporal processes with non-separable covariance structure. Scandinavian Journal of Statistics, 37 (4). pp. 553-567. ISSN 1467-9469

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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.

Item Type: Article
Journal or Publication Title: Scandinavian Journal of Statistics
Uncontrolled Keywords: convolution-based models ; non-separability ; spatio-temporal processes ; TIME DATA ; SPACE
Subjects: UNSPECIFIED
Departments: Faculty of Health and Medicine > Medicine
ID Code: 51914
Deposited By: ep_importer_pure
Deposited On: 08 Dec 2011 14:14
Refereed?: Yes
Published?: Published
Last Modified: 09 Apr 2014 22:55
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/51914

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