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.
Original language | English (US) |
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Pages (from-to) | 553-567 |
Number of pages | 15 |
Journal | Scandinavian Journal of Statistics |
Volume | 37 |
Issue number | 4 |
DOIs | |
State | Published - Dec 2010 |
Externally published | Yes |
Keywords
- Convolution-based models
- Non-separability
- Spatio-temporal processes
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty