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

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A class of convolution-based models for spatio-temporal processes with non-separable covariance structure. / Rodrigues, Alexandre; Diggle, Peter J.

In: Scandinavian Journal of Statistics, Vol. 37, No. 4, 12.2010, p. 553-567.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Rodrigues A, Diggle PJ. A class of convolution-based models for spatio-temporal processes with non-separable covariance structure. Scandinavian Journal of Statistics. 2010 Dec;37(4):553-567. doi: 10.1111/j.1467-9469.2009.00675.x

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Rodrigues, Alexandre ; Diggle, Peter J. / A class of convolution-based models for spatio-temporal processes with non-separable covariance structure. In: Scandinavian Journal of Statistics. 2010 ; Vol. 37, No. 4. pp. 553-567.

Bibtex

@article{178d4e7a50bb44eb805725061da34926,
title = "A class of convolution-based models for spatio-temporal processes with non-separable covariance structure",
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.",
keywords = "convolution-based models, non-separability, spatio-temporal processes, TIME DATA, SPACE",
author = "Alexandre Rodrigues and Diggle, {Peter J.}",
year = "2010",
month = dec,
doi = "10.1111/j.1467-9469.2009.00675.x",
language = "English",
volume = "37",
pages = "553--567",
journal = "Scandinavian Journal of Statistics",
issn = "0303-6898",
publisher = "Blackwell-Wiley",
number = "4",

}

RIS

TY - JOUR

T1 - A class of convolution-based models for spatio-temporal processes with non-separable covariance structure

AU - Rodrigues, Alexandre

AU - Diggle, Peter J.

PY - 2010/12

Y1 - 2010/12

N2 - 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.

AB - 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.

KW - convolution-based models

KW - non-separability

KW - spatio-temporal processes

KW - TIME DATA

KW - SPACE

U2 - 10.1111/j.1467-9469.2009.00675.x

DO - 10.1111/j.1467-9469.2009.00675.x

M3 - Journal article

VL - 37

SP - 553

EP - 567

JO - Scandinavian Journal of Statistics

JF - Scandinavian Journal of Statistics

SN - 0303-6898

IS - 4

ER -