Approximate Bayesian computation with functional statistics

Soubeyrand Samuel (), Carpentier Florence, Guiton François and Klein Etienne K.
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Soubeyrand Samuel: INRA, UR546 Biostatistics and Spatial Processes, F-84914 Avignon, France
Carpentier Florence: AgroParis Tech, Dpt. SVS, F-75231 Paris, France
Guiton François: INRA, UR546 Biostatistics and Spatial Processes, F-84914 Avignon, France
Klein Etienne K.: INRA, UR546 Biostatistics and Spatial Processes, F-84914 Avignon, France

Statistical Applications in Genetics and Molecular Biology, 2013, vol. 12, issue 1, 17-37

Abstract: Functional statistics are commonly used to characterize spatial patterns in general and spatial genetic structures in population genetics in particular. Such functional statistics also enable the estimation of parameters of spatially explicit (and genetic) models. Recently, Approximate Bayesian Computation (ABC) has been proposed to estimate model parameters from functional statistics. However, applying ABC with functional statistics may be cumbersome because of the high dimension of the set of statistics and the dependences among them. To tackle this difficulty, we propose an ABC procedure which relies on an optimized weighted distance between observed and simulated functional statistics. We applied this procedure to a simple step model, a spatial point process characterized by its pair correlation function and a pollen dispersal model characterized by genetic differentiation as a function of distance. These applications showed how the optimized weighted distance improved estimation accuracy. In the discussion, we consider the application of the proposed ABC procedure to functional statistics characterizing non-spatial processes.

Keywords: dispersal model; marked point process; pairwise genetic distance; parameter estimation; spatial model; TwoGener (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (2)

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DOI: 10.1515/sagmb-2012-0014

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