Dimitriou-Fakalou, C. (2009) Modelling data observed irregularly over space and regularly in time. Statistical Methodology, 6 (2). pp. 120-132. ISSN 1572-3127.
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When the data has been collected regularly over time and irregularly over space, it is difficult to impose an explicit auto-regressive structure over the space as it is over time. We study a phenomenon on a number of fixed locations. On each location the process forms an auto-regressive time series. The second-order dependence over space is reflected by the covariance matrix of the noise process, which is ‘white’ in time but not over the space. We consider the asymptotic properties of our inference methods, when the number of recordings in time only tends to infinity.
|Uncontrolled keywords:||Auto-regressive parameters; Best linear predictor; Yule–Walker estimators; Time-space separability|
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics|
|Depositing User:||Chrysoula Dimitriou Fakalou|
|Date Deposited:||10 Oct 2012 12:58|
|Last Modified:||12 Feb 2013 11:41|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/31485 (The current URI for this page, for reference purposes)|
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