Efficient computation of sparse and robust maximum association estimators

Pia Pfeiffer, Andreas Alfons and Peter Filzmoser

Computational Statistics & Data Analysis, 2025, vol. 207, issue C

Abstract: Robust statistical estimators offer resilience against outliers but are often computationally challenging, particularly in high-dimensional sparse settings. Modern optimization techniques are utilized for robust sparse association estimators without imposing constraints on the covariance structure. The approach splits the problem into a robust estimation phase, followed by optimization of a decoupled, biconvex problem to derive the sparse canonical vectors. An augmented Lagrangian algorithm, combined with a modified adaptive gradient descent method, induces sparsity through simultaneous updates of both canonical vectors. Results demonstrate improved precision over existing methods, with high-dimensional empirical examples illustrating the effectiveness of this approach. The methodology can also be extended to other robust sparse estimators.

Keywords: Biconvex optimization; Sparse robust canonical correlation; Robust estimation; Penalized canonical correlation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:207:y:2025:i:c:s016794732500009x

DOI: 10.1016/j.csda.2025.108133

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