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 1467-9469Full text not available from this repository.
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.
|Journal or Publication Title:||Scandinavian Journal of Statistics|
|Uncontrolled Keywords:||convolution-based models ; non-separability ; spatio-temporal processes ; TIME DATA ; SPACE|
|Departments:||Faculty of Health and Medicine > Medicine|
|Deposited On:||08 Dec 2011 14:14|
|Last Modified:||27 Apr 2017 02:44|
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