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.
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Notes
There is no difficulty in principle in allowing the sample space for X v to vary with v, and the single site scoring rule to vary with both v and x ∖v .
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We are grateful to Elias Krainski for his assistance with using Rcitrus.
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Dawid, A.P., Musio, M. Estimation of spatial processes using local scoring rules. AStA Adv Stat Anal 97, 173–179 (2013). https://doi.org/10.1007/s10182-012-0191-8
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DOI: https://doi.org/10.1007/s10182-012-0191-8