A User-Friendly Computational Framework for Robust Structured Regression with the L2 Criterion

We introduce a user-friendly computational framework for implementing robust versions of a wide variety of structured regression methods with the L 2 criterion. In addition to introducing an algorithm for performing L 2 E regression, our framework enables robust regression with the L 2 criterion for...

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Published in:Journal of computational and graphical statistics 2022, Vol.31 (4), p.1051-1062
Main Authors: Chi, Jocelyn T., Chi, Eric C.
Format: Article
Language:English
Subjects:
Algorithms
Block-relaxation
Convex optimization
Criteria
Minimum distance estimation
Parameter identification
Regression
Regularization
Robustness (mathematics)
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Chi, Eric C.
description We introduce a user-friendly computational framework for implementing robust versions of a wide variety of structured regression methods with the L 2 criterion. In addition to introducing an algorithm for performing L 2 E regression, our framework enables robust regression with the L 2 criterion for additional structural constraints, works without requiring complex tuning procedures on the precision parameter, can be used to identify heterogeneous subpopulations, and can incorporate readily available nonrobust structured regression solvers. We provide convergence guarantees for the framework and demonstrate its flexibility with some examples. Supplementary materials for this article are available online.
doi_str_mv 10.1080/10618600.2022.2035232
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source Taylor and Francis Science and Technology Collection
subjects Algorithms
Block-relaxation
Convex optimization
Criteria
Minimum distance estimation
Parameter identification
Regression
Regularization
Robustness (mathematics)
title A User-Friendly Computational Framework for Robust Structured Regression with the L2 Criterion
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