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September 18, 2025AxiomsOpen Access

Robust Adaptive Lasso via Robust Sample Autocorrelation Coefficient for the Autoregressive Models

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Novel robust adaptive lasso approach improves sparsity and outlier resistance in autoregressive models, indicating better performance.

Robust adaptive lasso method significantly enhances sparsity and resistance to outliers in autoregressive models.
The proposed method incorporates partial autocorrelation coefficients for adaptive penalties, promoting effective variable selection.
Numerical simulations demonstrate the method's promising performance, showing improvements in estimation accuracy under challenging conditions.
This approach potentially offers a solution to common challenges faced with classical estimation methods like least squares and maximum likelihood.

Jiang et al. (2025) studied this question.

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