Efficient Computation of Sparse and Robust Maximum Association Estimators

• URL: http://arxiv.org/abs/2311.17563v3
• Date: Wed, 29 Jan 2025 16:49:22 GMT
• Title: Efficient Computation of Sparse and Robust Maximum Association Estimators
• Authors: Pia Pfeiffer, Andreas Alfons, Peter Filzmoser,
• Abstract summary: Robust statistical estimators offer empirical precision but are often computationally challenging in high-dimensional sparse settings. Modern association estimator techniques are utilized for outliers without imposing resilience against other robust methods.
• Score: 0.4588028371034406
• License:
• 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.

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