IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v57y2016i3d10.1007_s00362-015-0674-2.html
   My bibliography  Save this article

A new method to build spatio-temporal covariance functions: analysis of ozone data

Author

Listed:
  • Mehdi Omidi

    (Tarbiat Modares University)

  • Mohsen Mohammadzadeh

    (Tarbiat Modares University)

Abstract

Statistical analysis of natural phenomena with spatial and temporal correlations requires the specification of the correlation structure via a covariance function. A separable spatio-temporal covariance function is usually used for the ease of application. Nonetheless, the separability of the spatio-temporal covariance function can be unrealistic in many settings, where it is required to use a non-separable spatio-temporal covariance function. In this paper, the role of Stieltjes transformation in the construction of non-separable spatio-temporal covariance function is investigated. Then, structural copula function is applied to construct a family of non-separable spatio-temporal covariance function. Afterwards, it is proved that this family of covariance functions does not possess any dimple which exists in some Gneiting’s models. Finally, a modified genetic algorithm is applied to explore the spatio-temporal correlation structure of Ozone data in Tehran, Iran.

Suggested Citation

  • Mehdi Omidi & Mohsen Mohammadzadeh, 2016. "A new method to build spatio-temporal covariance functions: analysis of ozone data," Statistical Papers, Springer, vol. 57(3), pages 689-703, September.
    • Handle: RePEc:spr:stpapr:v:57:y:2016:i:3:d:10.1007_s00362-015-0674-2
    DOI: 10.1007/s00362-015-0674-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00362-015-0674-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00362-015-0674-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Porcu, Emilio & Mateu, Jorge & Christakos, George, 2009. "Quasi-arithmetic means of covariance functions with potential applications to space-time data," Journal of Multivariate Analysis, Elsevier, vol. 100(8), pages 1830-1844, September.
    2. 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.
    3. 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.
    4. Frederik Michiels & Ann De Schepper, 2012. "How to improve the fit of Archimedean copulas by means of transforms," Statistical Papers, Springer, vol. 53(2), pages 345-355, May.
    5. Ma, Chunsheng, 2003. "Spatio-temporal stationary covariance models," Journal of Multivariate Analysis, Elsevier, vol. 86(1), pages 97-107, July.
    6. Dejian Lai, 2010. "Box–Cox transformation for spatial linear models: a study on lattice data," Statistical Papers, Springer, vol. 51(4), pages 853-864, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. José-María Montero & Gema Fernández-Avilés & Tiziana Laureti, 2021. "A Local Spatial STIRPAT Model for Outdoor NO x Concentrations in the Community of Madrid, Spain," Mathematics, MDPI, vol. 9(6), pages 1-33, March.
    2. Montero, José-María, 2018. "Geostatistics: Unde venis et quo vadis? /Geoestadística:¿De dónde vienes y a dónde vas?," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 36, pages 81-106, Enero.
    3. Monica Palma & Claudia Cappello & Sandra De Iaco & Daniela Pellegrino, 2019. "The residential real estate market in Italy: a spatio-temporal analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2451-2472, September.
    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. Porcu, E. & Mateu, J. & Zini, A. & Pini, R., 2007. "Modelling spatio-temporal data: A new variogram and covariance structure proposal," Statistics & Probability Letters, Elsevier, vol. 77(1), pages 83-89, January.
    6. 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.
    7. De Iaco, S., 2023. "Spatio-temporal generalized complex covariance models based on convolution," Computational Statistics & Data Analysis, Elsevier, vol. 183(C).
    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. Sandra De Iaco, 2011. "A new space--time multivariate approach for environmental data analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2471-2483, January.
    10. Ma, Chunsheng, 2004. "Spatial autoregression and related spatio-temporal models," Journal of Multivariate Analysis, Elsevier, vol. 88(1), pages 152-162, January.
    11. Taylor, Benjamin M. & Davies, Tilman M. & Rowlingson, Barry S. & Diggle, Peter J., 2013. "lgcp: An R Package for Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i04).
    12. Emilio Porcu & Moreno Bevilacqua & Marc G. Genton, 2016. "Spatio-Temporal Covariance and Cross-Covariance Functions of the Great Circle Distance on a Sphere," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 888-898, April.
    13. Christopher Withers & Saralees Nadarajah, 2013. "Correlation is first order independent of transformation," Statistical Papers, Springer, vol. 54(2), pages 443-456, May.
    14. 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.
    15. Durante Fabrizio & Fernández-Sánchez Juan & Trutschnig Wolfgang, 2014. "Solution to an open problem about a transformation on the space of copulas," Dependence Modeling, De Gruyter, vol. 2(1), pages 1-8, November.
    16. Hristo S. Sendov & Ričardas Zitikis, 2014. "The Shape of the Borwein–Affleck–Girgensohn Function Generated by Completely Monotone and Bernstein Functions," Journal of Optimization Theory and Applications, Springer, vol. 160(1), pages 67-89, January.
    17. Robinson, P.M. & Vidal Sanz, J., 2006. "Modified Whittle estimation of multilateral models on a lattice," Journal of Multivariate Analysis, Elsevier, vol. 97(5), pages 1090-1120, May.
    18. Yuan Wang & Brian P. Hobbs & Jianhua Hu & Chaan S. Ng & Kim‐Anh Do, 2015. "Predictive classification of correlated targets with application to detection of metastatic cancer using functional CT imaging," Biometrics, The International Biometric Society, vol. 71(3), pages 792-802, September.
    19. Miryam S. Merk & Philipp Otto, 2022. "Estimation of the spatial weighting matrix for regular lattice data—An adaptive lasso approach with cross‐sectional resampling," Environmetrics, John Wiley & Sons, Ltd., vol. 33(1), February.
    20. Tata Subba Rao & Sourav Das & Georgi N. Boshnakov, 2014. "A Frequency Domain Approach For The Estimation Of Parameters Of Spatio-Temporal Stationary Random Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(4), pages 357-377, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stpapr:v:57:y:2016:i:3:d:10.1007_s00362-015-0674-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.