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.
Full text not available from this repository.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: | 26 Jul 2012 19:58 |
| Identification Number: | |
| URI: | http://eprints.lancs.ac.uk/id/eprint/51914 |
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