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A comparison of four MIO formulations for outlier-robust regression in Julia, with synthetic data generation for testing. All four greatly improve over OLS on signal recovery in presence of outliers.

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azaccor/Outlier_Robust_Regression_Models

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Outlier_Robust_Regression_Models

A comparison of four outlier-robust regression formulations using mixed integer optimization in Julia

This code was written by Ali Borenstein and Austin Zaccor as part of a final project in Machine Learning Under a Modern Optimization Lens at MIT as part of their Masters of Business Analytics degree. Please reach out to either of us on github or LinkedIn with questions about the code or mathematical formulations. As mentioned in the main file, publication is forthcoming.

Data

Synthetic datasets are available in Data folder, synthetic data generation process available in "Data Generation Function Final.ipynb"

Other datasets used in this file are as follows:

Hertzsprung-Russell Start Dataset http://support.sas.com/documentation/cdl/en/imlug/64248/HTML/default/viewer.htm#imlug_robustregexpls_sect003.htm

Stackloss of Brownlee: http://support.sas.com/documentation/cdl/en/imlug/65547/HTML/default/viewer.htm#imlug_robustregexpls_sect012.htm

QSAR Aquatic Toxicity (Retrieved from UCI Machine Learning Repository): https://archive.ics.uci.edu/ml/datasets/QSAR+aquatic+toxicity

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A comparison of four MIO formulations for outlier-robust regression in Julia, with synthetic data generation for testing. All four greatly improve over OLS on signal recovery in presence of outliers.

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