Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks

@article{Hausser2009EntropyIA,
  title={Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks},
  author={J. Hausser and K. Strimmer},
  journal={J. Mach. Learn. Res.},
  year={2009},
  volume={10},
  pages={1469-1484}
}
  • J. Hausser, K. Strimmer
  • Published 2009
  • Mathematics, Computer Science
  • J. Mach. Learn. Res.
  • 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… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 46 REFERENCES
    A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics
    1360
    Accurate Ranking of Differentially Expressed Genes by a Distribution-Free Shrinkage Approach
    183
    An empirical Bayes approach to inferring large-scale gene association networks
    789
    From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data
    323
    Coverage-adjusted entropy estimation.
    45
    Information-Theoretic Inference of Large Transcriptional Regulatory Networks
    358
    Simultaneous Estimation of Multinomial Cell Probabilities
    77
    Sparse graphical models for exploring gene expression data
    435
    High-dimensional graphs and variable selection with the Lasso
    2777