Mosammam, AM and Kent, JT (2016) Estimation and testing for covariance-spectral spatial-temporal models. Environmental and Ecological Statistics, 23 (1). pp. 43-64. ISSN 1352-8505
Abstract
In this paper we explore a covariance-spectral modelling strategy for spatial-temporal processes which involves a spectral approach for time but a covariance approach for space. It facilitates the analysis of coherence between the temporal frequency components at different spatial sites. Stein (J R Stat Soc Ser B (Statistical Methodology) 67:667–687, 2005) developed a semi-parametric model within this framework. The purpose of this paper is to give a deeper insight into the properties of his model and to develop simpler and more intuitive methods of estimation and testing. A very neat estimation for drift direction is proposed while Stein assumes it is known. An example is given using the Irish wind speed data. Stein constructed various plot to assess the goodness of fit of the model, we use similar plots to estimates the parameters.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015, Springer Science+Business Media New York. This is an author produced version of a paper published in Environmental and Ecological Statistics. Uploaded in accordance with the publisher's self-archiving policy. The final publication is available at Springer via http://dx.doi.org/10.1007/s10651-015-0322-y. |
Keywords: | Asymmetry; Coherence function; Covariance-spectral model; Space-time model |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Sep 2015 12:01 |
Last Modified: | 29 Oct 2016 06:27 |
Published Version: | http://dx.doi.org/10.1007/s10651-015-0322-y |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/s10651-015-0322-y |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:89424 |