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Poisson Kriging

Author

Listed:
  • Victor De Oliveira

    (UTSA)

Abstract

This work revisits a simple model for geostatistical count data and make explicit the assumptions under which the model is constructed. We review the parameter estimators and predictors for a latent that appear in the literature, and propose new estimators and predictors. Finally, we plan to carry a detailed simulation experiment to investigate and compare the statistical properties of the different parameter estimators and predictors.

Suggested Citation

  • Victor De Oliveira, 2013. "Poisson Kriging," Working Papers 0183mss, College of Business, University of Texas at San Antonio.
    • Handle: RePEc:tsa:wpaper:0183mss
    as

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    File URL: http://interim.business.utsa.edu/wps/mss/0032MSS-496-2013.pdf
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    References listed on IDEAS

    as
    1. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-720, May.
    2. Varin, Cristiano & Host, Gudmund & Skare, Oivind, 2005. "Pairwise likelihood inference in spatial generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1173-1191, June.
    3. William F. Christensen, 2011. "Filtered Kriging for Spatial Data with Heterogeneous Measurement Error Variances," Biometrics, The International Biometric Society, vol. 67(3), pages 947-957, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Geostatistics; Nugget e_ect; Ordinary kriging; Semivariogram; Spatial count data;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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