Research output: Contribution to Journal/Magazine › Journal article › peer-review
<mark>Journal publication date</mark> | 12/2010 |
---|---|
<mark>Journal</mark> | Scandinavian Journal of Statistics |
Issue number | 4 |
Volume | 37 |
Number of pages | 15 |
Pages (from-to) | 553-567 |
Publication Status | Published |
<mark>Original language</mark> | English |
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.