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Intensity Estimation for Spatial Point Processes Observed with Noise

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  • LIONEL CUCALA

Abstract

. This article introduces a kernel estimator of the intensity function of spatial point processes taking into account location errors. The asymptotic properties of the estimator are derived and a bandwidth selection procedure is described. A simulation study compares our results with that of the classical kernel estimator and shows that the edge‐corrected deconvoluting kernel estimator is more appropriate.

Suggested Citation

  • Lionel Cucala, 2008. "Intensity Estimation for Spatial Point Processes Observed with Noise," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(2), pages 322-334, June.
    • Handle: RePEc:bla:scjsta:v:35:y:2008:i:2:p:322-334
    DOI: 10.1111/j.1467-9469.2007.00583.x
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    Cited by:

    1. Julie McIntyre & Ronald P. Barry, 2012. "Bivariate deconvolution with SIMEX: an application to mapping Alaska earthquake density," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 297-308, April.
    2. Isabel Fuentes-Santos & Wenceslao González-Manteiga & Jorge Mateu, 2016. "Consistent Smooth Bootstrap Kernel Intensity Estimation for Inhomogeneous Spatial Poisson Point Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 416-435, June.
    3. Marie-Colette N. M. Lieshout, 2012. "On Estimation of the Intensity Function of a Point Process," Methodology and Computing in Applied Probability, Springer, vol. 14(3), pages 567-578, September.

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