title: Approximately sufficient statistics and bayesian computation.
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.'
authors.affiliation: University of Idaho. joyce@uidaho.edu
authors.forename: Paul
authors.initials: P
authors.lastname: Joyce
authors.forename: Paul
authors.initials: P
authors.lastname: Marjoram