Abstract

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.'

Submitted: June 4, 2008 · Accepted: July 7, 2008 · Published: August 30, 2008

Recommended Citation

Joyce, Paul and Marjoram, Paul (2008) "Approximately Sufficient Statistics and Bayesian Computation," Statistical Applications in Genetics and Molecular Biology: Vol. 7 : Iss. 1, Article 26.
DOI: 10.2202/1544-6115.1389
Available at: http://www.bepress.com/sagmb/vol7/iss1/art26