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

Rodrigues, A and Diggle, PJ (2010) A class of convolution-based model for spatio-temporal processes with non-separable covariance structure. Scandinavian Journal of Statistics, 37 (4). pp. 553-567. ISSN 0303-6898

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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
Uncontrolled Keywords:convolution-based models, non-separability, spatio-temporal processes
Subjects:5. Quantitative Data Handling and Data Analysis > 5.9 Spatial Data Analysis
ID Code:845
Deposited By: L-W-S user
Deposited On:19 May 2010 13:47
Last Modified:09 Feb 2012 15:06

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