Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks

Details Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks

2008-11-21

arxiv.org/abs/0811.3579v3

Journal of Machine Learning Research 10: 1469-1484 (2009)

Statistics

Machine Learning

18 pages, 3 figures, 1 table

Scientific paper

We present a procedure for effective estimation of entropy and mutual information from small-sample data, and apply it to the problem of inferring high-dimensional gene association networks. Specifically, we develop a James-Stein-type shrinkage estimator, resulting in a procedure that is highly efficient statistically as well as computationally. Despite its simplicity, we show that it outperforms eight other entropy estimation procedures across a diverse range of sampling scenarios and data-generating models, even in cases of severe undersampling. We illustrate the approach by analyzing E. coli gene expression data and computing an entropy-based gene-association network from gene expression data. A computer program is available that implements the proposed shrinkage estimator.

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