{"ID":2830476,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.11139","arxiv_id":"2512.11139","title":"Autotune: fast, accurate, and automatic tuning parameter selection for Lasso","abstract":"Least absolute shrinkage and selection operator (Lasso), a popular method for high-dimensional regression, is now used widely for estimating high-dimensional time series models such as the vector autoregression (VAR). Selecting its tuning parameter efficiently and accurately remains a challenge, despite the abundance of available methods for doing so. We propose $\\mathsf{autotune}$, a strategy for Lasso to automatically tune itself by optimizing a penalized Gaussian log-likelihood alternately over regression coefficients and noise standard deviation. Using extensive simulation experiments on regression and VAR models, we show that $\\mathsf{autotune}$ is faster, and provides better generalization and model selection than established alternatives in low signal-to-noise regimes. In the process, $\\mathsf{autotune}$ provides a new estimator of noise standard deviation that can be used for high-dimensional inference, and a new visual diagnostic procedure for checking the sparsity assumption on regression coefficients. Finally, we demonstrate the utility of $\\mathsf{autotune}$ on a real-world financial data set. An R package based on C++ is also made publicly available on Github.","short_abstract":"Least absolute shrinkage and selection operator (Lasso), a popular method for high-dimensional regression, is now used widely for estimating high-dimensional time series models such as the vector autoregression (VAR). Selecting its tuning parameter efficiently and accurately remains a challenge, despite the abundance o...","url_abs":"https://arxiv.org/abs/2512.11139","url_pdf":"https://arxiv.org/pdf/2512.11139v2","authors":"[\"Tathagata Sadhukhan\",\"Ines Wilms\",\"Stephan Smeekes\",\"Sumanta Basu\"]","published":"2025-12-11T22:00:12Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"stat.ML\"]","methods":"[]","has_code":false}