On-line spatio-temporal prediction by a state space representation of the generalised space time autoregressive model

Metron

Volumn 59, Issue 1-2, 2001, Pages 157-168

  Ippoliti, Luigi ^a  

^a UNIVERSITY OF CHIETI   (Italy)

Author keywords

Cross validation; Dynamic model; Kriging; Spatio Temporal modelling

Indexed keywords

EID: 84879142819     PISSN: 00261424     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (23)

1. Bennett, R. J. (1979) Spatial time series, Pion Limited.
2. Carrol, S. S. and Cressie, N. (1996) A comparison of geostatistical methodologies used to estimate snow water equivalent, Water Resour. Bull., 32, 267-78.
3. Cressie, N. (1993) Statistics for spatial data, Wiley, New York.
4. Cressie, N. and Huang, (1999) Classes of Nonseparable, Spatio-Temporal Stationary Covariance Functions, J.A.S.A., vol. 94, n. 448,1330-1340.
5. Cristakos, G. (1981) A theory of spatio-temporal random fields and its application to space-time data processing, IEEE Transactions on Systems, Man, and Cybernetics, 21, 861-875.
6. De Cesare, L., Myers D. E. and Posa, D. (1996) Spatial-Temporal Modeling of SO2, in Milan district, in GeostatisticsWollong '96, E. Y. Baafi and N. A. Schofield (eds), Kluwer Academic Publisher.
7. De Cesare, L., Myers, D. E. and Posa, D. (2001) Product-sum covariance for spacetime modelling: an environmental application. Forthcoming in Environmetrics (2001), 12, 11-23.
8. De Cesare, L., Myers, D. E. and Posa, D. (2001) Estimating and modelling space-time correlation structures. Forthcoming in Statistics and Probability Letters (2001), 51, N.1, 9-14.
9. Di Giacinto, V. (1995) Sulla costruzione di modelli lineari per lanalisi di dati economici a struttura spazio-temporale, Ph.D. Thesis.
10. Guttorp, P., Mairing, W. and Sampson, P. D. (1994) A space-time analysis of groundlevel ozone data, Environmetrics, 5, 241-254.
11. Ippoliti, L., Romagnoli, L. and Redfern, E. (1998) Kalman Filter on Generalised STARMA model, Technical Report, Department of Statistics, Leeds University.
12. Lutkepohl, H. (1991) Introduction to Multiple Time Series Analysis, Springer-Verlag.
13. Mardia, K., Redfern, E., Goodall, C. and Alonso, F. (1998) The Kriged Kalman Filter, TEST, vol. 7 n. 2, 217-285.
14. Martin, R. L. and Oppen, J. E. (1975) The identification of regional forecasting models using space-time correlation functions, Trans. Inst. British Geographers, 66, 95- 11 8.
15. Pace, K. and Zou, D. (2000) Closed-form Maximum Likelihood Estimates of Nearest Neighbour Spatial Dependence, Geographical Analysis, 32 n. 2.
16. Pfeifer, P. E. and Deutsch, S. J. (1980)Athree-stage iterative procedure for space-time modelling, Technometrics, 22, 35-47.
17. Posa, D. (1995) Introduzione alla Geostatistica, Adriatica Editrice Salentina, Lecce.
18. Stoffer, D. S. (1985) Maximum likelihood fitting of STARMAX models to incomplete space-time series data, in Time Series Analysis: theory and practice 6, O.D. Anderson, J.K. Ord and E.A. Robinson (eds) North-Holland.
19. Terzi, S. (1995) Maximum Likelihood estimation of a generalised STAR(p, lp) model, Journal of the Italian Statistical Society, vol. 4, n. 3.
20. Young, P. C. (1974) Recursive approaches to time series analysis, Bulletin of the Institute of Mathematics and its Applications, 10, 209-224.
21. Wackernagel H. (1998) Multivariate Geostatistics, Springer-Verlag, Berlino.
22. Whittle, P. (1954) On stationary processes in the plane, Biometrika, 41, 434-449.
23. Wickle, K. and Cressie N. (1997) A dimension approach to space-time Kalman filtering, Preprint Statistical Laboratory, ISU, n. 97-24.