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The Optimistic Method for Model Estimation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9897)

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.

Keywords

Ordinary Little Square Ising Model Robust Optimization Optimistic Estimator Interior Point Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.The MITRE CorporationBedfordUSA
  2. 2.Stanford UniversityStanfordUSA

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