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A Bayesian Approach to Parameter Estimation in the Presence of Spatial Missing Data

Author

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  • Domenica Panzera
  • Roberto Benedetti
  • Paolo Postiglione

Abstract

The missing data problem has been widely addressed in the literature. The traditional methods for handling missing data may be not suited to spatial data, which can exhibit distinctive structures of dependence and/or heterogeneity. As a possible solution to the spatial missing data problem, this paper proposes an approach that combines the Bayesian Interpolation method [Benedetti, R. & Palma, D. (1994) Markov random field-based image subsampling method, Journal of Applied Statistics , 21(5), 495--509] with a multiple imputation procedure. The method is developed in a univariate and a multivariate framework, and its performance is evaluated through an empirical illustration based on data related to labour productivity in European regions.

Suggested Citation

  • Domenica Panzera & Roberto Benedetti & Paolo Postiglione, 2016. "A Bayesian Approach to Parameter Estimation in the Presence of Spatial Missing Data," Spatial Economic Analysis, Taylor & Francis Journals, vol. 11(2), pages 201-218, June.
    • Handle: RePEc:taf:specan:v:11:y:2016:i:2:p:201-218
    DOI: 10.1080/17421772.2016.1102962
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    References listed on IDEAS

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    1. Griffith, Daniel A. & Layne, Larry J., 1999. "A Casebook for Spatial Statistical Data Analysis: A Compilation of Different Thematic Data Sets," OUP Catalogue, Oxford University Press, number 9780195109580.
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    Cited by:

    1. Marcos Sanso-Navarro & María Vera-Cabello & Miguel Puente-Ajovín, 2020. "Regional convergence and spatial dependence: a worldwide perspective," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 65(1), pages 147-177, August.
    2. Paolo Postiglione & Alfredo Cartone & Domenica Panzera, 2020. "Economic Convergence in EU NUTS 3 Regions: A Spatial Econometric Perspective," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    3. Alberto Díaz Dapena & Esteban Fernández Vázquez & Fernando Rubiera Morollón & Ana Viñuela, 2021. "Mapping poverty at the local level in Europe: A consistent spatial disaggregation of the AROPE indicator for France, Spain, Portugal and the United Kingdom," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 63-81, February.
    4. Prince Allotey & Ofer Harel, 2023. "Bayesian Spatial Modeling of Incomplete Data with Application to HIV Prevalence in Ghana," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(2), pages 307-329, November.

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