IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v14y2005i1d10.1007_bf02511575.html
   My bibliography  Save this article

Non parametric space-time modeling of SO2 in presence of many missing data

Author

Listed:
  • Bruno Scarpa

    (Università di Pavia)

Abstract

Given pollution measurement from a network of monitoring sites in the area of a city and over an extended period of time, an important problem is to identify the spatial and temporal structure of the data. In this paper we focus on the identification and estimate of a statistical non parametric model to analyse the SO2 in the city of Padua, where data are collected by some fixed stations and some mobile stations moving without any specific rule in different new locations. The impact of the use of mobile stations is that for each location there are times when data was not collected. Assuming temporal stationarity and spatial isotropy for the residuals of an additive model for the logarithm of SO2 concentration, we estimate the semivariogram using a kernel-type estimator. Attempts are made to avoid the assumption of spatial isotropy. Bootstrap confidence bands are obtained for the spatial component of the additive model that is a deterministic function which defines the spatial structure. Finally, an example is proposed to design an optimal network for the mobiles monitoring stations in a fixed future time, given all the information available.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:stmapp:v:14:y:2005:i:1:d:10.1007_bf02511575
    DOI: 10.1007/BF02511575
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/BF02511575
    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/BF02511575?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. De Iaco, S. & Myers, D. E. & Posa, D., 2002. "Space-time variograms and a functional form for total air pollution measurements," Computational Statistics & Data Analysis, Elsevier, vol. 41(2), pages 311-328, December.
    2. 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.
    3. Giuseppe Arbia & Giovanni Lafratta, 2002. "Anisotropic spatial sampling designs for urban pollution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(2), pages 223-234, May.
    4. Cleveland, William S. & Devlin, Susan J. & Grosse, Eric, 1988. "Regression by local fitting : Methods, properties, and computational algorithms," Journal of Econometrics, Elsevier, vol. 37(1), pages 87-114, January.
    5. E. E. Kammann & M. P. Wand, 2003. "Geoadditive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 1-18, January.
    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. 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.
    2. Sandra De Iaco, 2010. "Space-time correlation analysis: a comparative study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(6), pages 1027-1041.
    3. 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.
    4. 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.
    5. Lee, Dae-Jin & Durbán, María, 2009. "P-spline anova-type interaction models for spatio-temporal smoothing," DES - Working Papers. Statistics and Econometrics. WS ws093312, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Bell, David & Kay, Jim & Malley, Jim, 1996. "A non-parametric approach to non-linear causality testing," Economics Letters, Elsevier, vol. 51(1), pages 7-18, April.
    7. John T. Barkoulas & Christopher F. Baum & Joseph Onochie, 1997. "A nonparametric investigation of the 90‐day t‐bill rate," Review of Financial Economics, John Wiley & Sons, vol. 6(2), pages 187-198.
    8. Levine, Oliver, 2017. "Acquiring growth," Journal of Financial Economics, Elsevier, vol. 126(2), pages 300-319.
    9. Carter, Michael R. & Zimmerman, Frederick J., 2000. "The dynamic cost and persistence of asset inequality in an agrarian economy," Journal of Development Economics, Elsevier, vol. 63(2), pages 265-302, December.
    10. Carter, Michael R. & May, Julian, 1999. "Poverty, livelihood and class in rural South Africa," World Development, Elsevier, vol. 27(1), pages 1-20, January.
    11. Erik Snowberg & Justin Wolfers, 2010. "Explaining the Favorite-Long Shot Bias: Is it Risk-Love or Misperceptions?," Journal of Political Economy, University of Chicago Press, vol. 118(4), pages 723-746, August.
    12. LE GALLO, Julie, 2000. "Econométrie spatiale 2 -Hétérogénéité spatiale," LATEC - Document de travail - Economie (1991-2003) 2001-01, LATEC, Laboratoire d'Analyse et des Techniques EConomiques, CNRS UMR 5118, Université de Bourgogne.
    13. William R. Emmons & Frank A. Schmid, 2004. "When for-profits and not-for-profits compete: theory and empirical evidence from retail banking," Supervisory Policy Analysis Working Papers 2004-01, Federal Reserve Bank of St. Louis.
    14. Yee Leung & Chang-Lin Mei & Wen-Xiu Zhang, 2000. "Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model," Environment and Planning A, , vol. 32(1), pages 9-32, January.
    15. Abbi Kedir & Richard Disney & Indraneel Dasgupta, 2011. "Why Use Roscas When You Can Use Banks? Theory And Evidence From Ethiopia," Discussion Papers 11/05, University of Nottingham, CREDIT.
    16. Richard A. Meese & Andrew K. Rose, 1991. "An Empirical Assessment of Non-Linearities in Models of Exchange Rate Determination," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(3), pages 603-619.
    17. Gorton, Gary & Schmid, Frank A., 2000. "Universal banking and the performance of German firms," Journal of Financial Economics, Elsevier, vol. 58(1-2), pages 29-80.
    18. Miguel Ángel García & Ivan Muñiz, 2005. "El impacto espacial de las economías de aglomeración y su efecto sobre la estructura urbana.El caso de la industria en Barcelona, 1986-1996," Working Papers wpdea0509, Department of Applied Economics at Universitat Autonoma of Barcelona.
    19. De Iaco, S. & Palma, M. & Posa, D., 2005. "Modeling and prediction of multivariate space-time random fields," Computational Statistics & Data Analysis, Elsevier, vol. 48(3), pages 525-547, March.
    20. Somnath Das & Baruch Lev, 1994. "Nonlinearity in the Returns†Earnings Relation: Tests of Alternative Specifications and Explanations," Contemporary Accounting Research, John Wiley & Sons, vol. 11(1), pages 353-379, June.

    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:stmapp:v:14:y:2005:i:1:d:10.1007_bf02511575. 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.