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Estimating and modeling space-time correlation structures

Author

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  • Cesare, L. De
  • Myers, D. E.
  • Posa, D.

Abstract

In this paper, a class of product-sum covariance models has been introduced for estimating and modeling space-time correlation structures. It is shown how the coefficients of this class of models are related to the global sill and "partial" spatial and temporal sills; moreover, some constraints on these sills have been given in order to assure positive definiteness of the product-sum covariance model. A brief comparative study with some other classes of spatial-temporal covariance models has been pointed out.

Suggested Citation

  • Cesare, L. De & Myers, D. E. & Posa, D., 2001. "Estimating and modeling space-time correlation structures," Statistics & Probability Letters, Elsevier, vol. 51(1), pages 9-14, January.
    • Handle: RePEc:eee:stapro:v:51:y:2001:i:1:p:9-14
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    References listed on IDEAS

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    1. Posa, D., 1993. "A simple description of spatial-temporal processes," Computational Statistics & Data Analysis, Elsevier, vol. 15(4), pages 425-437, May.
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    Cited by:

    1. Raquel Menezes & Helena Piairo & Pilar García-Soidán & Inês Sousa, 2016. "Spatial–temporal modellization of the $$\hbox {NO}_{2}$$ NO 2 concentration data through geostatistical tools," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 107-124, March.
    2. Raquel Menezes & Helena Piairo & Pilar García-Soidán & Inês Sousa, 2016. "Spatial–temporal modellization of the $$\hbox {NO}_{2}$$ NO 2 concentration data through geostatistical tools," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 107-124, March.
    3. Ma, Chunsheng, 2003. "Nonstationary covariance functions that model space-time interactions," Statistics & Probability Letters, Elsevier, vol. 61(4), pages 411-419, February.
    4. Sandra De Iaco, 2010. "Space-time correlation analysis: a comparative study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(6), pages 1027-1041.
    5. Alexandre Rodrigues & Peter J. Diggle, 2010. "A Class of Convolution‐Based Models for Spatio‐Temporal Processes with Non‐Separable Covariance Structure," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(4), pages 553-567, December.
    6. Villez, Kris & Del Giudice, Dario & Neumann, Marc B. & Rieckermann, Jörg, 2020. "Accounting for erroneous model structures in biokinetic process models," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    7. P. Gregori & E. Porcu & J. Mateu & Z. Sasvári, 2008. "On potentially negative space time covariances obtained as sum of products of marginal ones," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(4), pages 865-882, December.
    8. Firoozeh Rivaz & Mohsen Mohammadzadeh & Majid Jafari Khaledi, 2011. "Spatio-temporal modeling and prediction of CO concentrations in Tehran city," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1995-2007, November.
    9. Bardia Bayat & Mohsen Nasseri & Banafsheh Zahraie, 2015. "Identification of long-term annual pattern of meteorological drought based on spatiotemporal methods: evaluation of different geostatistical approaches," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 76(1), pages 515-541, March.
    10. Iaco, S. De & Myers, D. E. & Posa, D., 2001. "Space-time analysis using a general product-sum model," Statistics & Probability Letters, Elsevier, vol. 52(1), pages 21-28, March.
    11. Rui Jiang & Chunxue Liu & Xiaowei Liu & Shuai Zhang, 2022. "Space–Time Effect of Green Total Factor Productivity in Mineral Resources Industry in China: Based on Space–Time Semivariogram and SPVAR Model," Sustainability, MDPI, vol. 14(14), pages 1-16, July.
    12. Bruno Scarpa, 2005. "Non parametric space-time modeling of SO2 in presence of many missing data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 14(1), pages 67-82, February.

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