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

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<mark>Journal publication date</mark>12/2010
<mark>Journal</mark>Scandinavian Journal of Statistics
Issue number4
Volume37
Number of pages15
Pages (from-to)553-567
Publication StatusPublished
<mark>Original language</mark>English

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.