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A weighted polynomial regression method for local fitting of spatial data

Author

Listed:
  • Tonino Sclocco

    (Universitá G. d’Annunzio)

  • Marco Marzio

    (Universitá G. d’Annunzio)

Abstract

. Typically, parametric approaches to spatial problems require restrictive assumptions. On the other hand, in a wide variety of practical situations nonparametric bivariate smoothing techniques has been shown to be successfully employable for estimating small or large scale regularity factors, or even the signal content of spatial data taken as a whole. We propose a weighted local polynomial regression smoother suitable for fitting of spatial data. To account for spatial variability, we both insert a spatial contiguity index in the standard formulation, and construct a spatial-adaptive bandwidth selection rule. Our bandwidth selector depends on the Geary’s local indicator of spatial association. As illustrative example, we provide a brief Monte Carlo study case on equally spaced data, the performances of our smoother and the standard polynomial regression procedure are compared.

Suggested Citation

  • Tonino Sclocco & Marco Marzio, 2004. "A weighted polynomial regression method for local fitting of spatial data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 13(3), pages 315-325, December.
    • Handle: RePEc:spr:stmapp:v:13:y:2004:i:3:d:10.1007_s10260-003-0066-y
    DOI: 10.1007/s10260-003-0066-y
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    Cited by:

    1. Fabio Crosilla & Domenico Visintini & Francesco Sepic, 2007. "An automatic classification and robust segmentation procedure of spatial objects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(3), pages 329-341, February.
    2. Fabio Crosilla & Domenico Visintini & Francesco Sepic, 2007. "An automatic classification and robust segmentation procedure of spatial objects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(3), pages 329-341, February.

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