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The Optimistic Method for Model Estimation

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Advances in Intelligent Data Analysis XV (IDA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9897))

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Abstract

We present the method of optimistic estimation, a novel paradigm that seeks to incorporate robustness to errors-in-variables biases directly into the estimation objective function. This approach protects parameter estimates in statistical models from data set corruption. We apply the optimistic paradigm to estimation of linear regression, logistic regression, and Ising graphical models in the presence of noise and demonstrate that more accurate predictions of the model parameters can be obtained.

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Acknowledgments

The authors wish to thank Abigail Gertner and Jason Ventrella of The MITRE Corporation for helpful comments and recommendations. The author’s affiliation with The MITRE Corporation is provided for identification purposes only, and is not intended to convey or imply MITRE’s concurrence with, or support for, the positions, opinions or viewpoints expressed by the author. Approved for Public Release; Distribution Unlimited. Case Number 16-0621.

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Authors and Affiliations

  1. The MITRE Corporation, Bedford, MA, 01730, USA

    James Brofos & Frank Zhang

  2. Stanford University, Stanford, CA, 94305, USA

    Rui Shu

Authors
  1. James Brofos
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  2. Rui Shu
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  3. Frank Zhang
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Corresponding author

Correspondence to James Brofos .

Editor information

Editors and Affiliations

  1. Stockholm University , Stockholm, Sweden

    Henrik Boström

  2. Leiden University , Leiden, The Netherlands

    Arno Knobbe

  3. University of Porto , Porto, Portugal

    Carlos Soares

  4. Stockholm University , Stockholm, Sweden

    Panagiotis Papapetrou

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Brofos, J., Shu, R., Zhang, F. (2016). The Optimistic Method for Model Estimation. In: Boström, H., Knobbe, A., Soares, C., Papapetrou, P. (eds) Advances in Intelligent Data Analysis XV. IDA 2016. Lecture Notes in Computer Science(), vol 9897. Springer, Cham. https://doi.org/10.1007/978-3-319-46349-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-46349-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46348-3

  • Online ISBN: 978-3-319-46349-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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