Electronic Journal of Statistics

A general family of trimmed estimators for robust high-dimensional data analysis

Eunho Yang, Aurélie C. Lozano, and Aleksandr Aravkin

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We consider the problem of robustifying high-dimensional structured estimation. Robust techniques are key in real-world applications which often involve outliers and data corruption. We focus on trimmed versions of structurally regularized M-estimators in the high-dimensional setting, including the popular Least Trimmed Squares estimator, as well as analogous estimators for generalized linear models and graphical models, using convex and non-convex loss functions. We present a general analysis of their statistical convergence rates and consistency, and then take a closer look at the trimmed versions of the Lasso and Graphical Lasso estimators as special cases. On the optimization side, we show how to extend algorithms for M-estimators to fit trimmed variants and provide guarantees on their numerical convergence. The generality and competitive performance of high-dimensional trimmed estimators are illustrated numerically on both simulated and real-world genomics data.

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Electron. J. Statist., Volume 12, Number 2 (2018), 3519-3553.

Received: March 2018
First available in Project Euclid: 22 October 2018

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Lasso robust estimation high-dimensional variable selection sparse learning

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Yang, Eunho; Lozano, Aurélie C.; Aravkin, Aleksandr. A general family of trimmed estimators for robust high-dimensional data analysis. Electron. J. Statist. 12 (2018), no. 2, 3519--3553. doi:10.1214/18-EJS1470. https://projecteuclid.org/euclid.ejs/1540195547

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