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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

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Approximately Sufficient Statistics and Bayesian Computation

Paul Joyce1 / Paul Marjoram2

1University of Idaho


Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 7, Issue 1, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1389, August 2008

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The analysis of high-dimensional data sets is often forced to rely upon well-chosen summary statistics. A systematic approach to choosing such statistics, which is based upon a sound theoretical framework, is currently lacking. In this paper we develop a sequential scheme for scoring statistics according to whether their inclusion in the analysis will substantially improve the quality of inference. Our method can be applied to high-dimensional data sets for which exact likelihood equations are not possible. We illustrate the potential of our approach with a series of examples drawn from genetics. In summary, in a context in which well-chosen summary statistics are of high importance, we attempt to put the `well' into `chosen.'

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