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Estimation of spatial processes using local scoring rules

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  • A. Dawid
  • Monica Musio

Abstract

We display pseudo-likelihood as a special case of a general estimation technique based on proper scoring rules. Such a rule supplies an unbiased estimating equation for any statistical model, and this can be extended to allow for missing data. When the scoring rule has a simple local structure, as in many spatial models, the need to compute problematic normalising constants is avoided. We illustrate the approach through an analysis of data on disease in bell pepper plants. Copyright Springer-Verlag 2013

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  • A. Dawid & Monica Musio, 2013. "Estimation of spatial processes using local scoring rules," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(2), pages 173-179, April.
    • Handle: RePEc:spr:alstar:v:97:y:2013:i:2:p:173-179
    DOI: 10.1007/s10182-012-0191-8
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    References listed on IDEAS

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    1. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    2. A. Dawid, 2007. "The geometry of proper scoring rules," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(1), pages 77-93, March.
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    Cited by:

    1. Alexander Dawid & Monica Musio, 2014. "Theory and applications of proper scoring rules," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 169-183, August.
    2. A. Philip Dawid & Monica Musio & Laura Ventura, 2016. "Minimum Scoring Rule Inference," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 123-138, March.

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