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Efficient computation of sparse and robust maximum association estimators

Author

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  • Pfeiffer, Pia
  • Alfons, Andreas
  • Filzmoser, Peter

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.

Suggested Citation

  • Pfeiffer, Pia & Alfons, Andreas & Filzmoser, Peter, 2025. "Efficient computation of sparse and robust maximum association estimators," Computational Statistics & Data Analysis, Elsevier, vol. 207(C).
  • Handle: RePEc:eee:csdana:v:207:y:2025:i:c:s016794732500009x
    DOI: 10.1016/j.csda.2025.108133
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