The paper introduces μs’ autotune, an automatic tuning strategy for the Lasso that optimizes a penalized Gaussian log‑likelihood over regression coefficients and noise standard deviation. Extensive simulations on regression and VAR models show that autotune is faster and yields better generalization and model selection, especially in low signal‑to‑noise regimes. The method also delivers a new noise‑standard‑deviation estimator, a visual diagnostic for sparsity, and is demonstrated on a real‑world financial dataset, with an accompanying R package available on GitHub.
By Tathagata Sadhukhan, Ines Wilms, Stephan Smeekes, Sumanta Basu
The paper introduces a feature selection framework for high‑dimensional financial regression that uses Monte Carlo Tree Search with Generalized Nested Rollout Policy Adaptation. It treats feature selection as a sequential decision process and evaluates candidate subsets by out‑of‑sample validation performance, incorporating subset size and multicollinearity into a lexicographic objective. Applied to a NASDAQ‑100 weekly return regression with 1,072 engineered features, the method outperforms an optimized Lasso baseline, achieving an out‑of‑sample R² of 0.6961 versus 0.6313.
By Cameron Mouangue, Tristan Cazenave
arXiv:2309. 15769v3 Announce Type: replace-cross Abstract: Recent advances in deep learning have highlighted the phenomenon of benign overfitting in overparameterized statistical models, sparking significant interest in understanding its foundations.
By Dennis Shen, Dogyoon Song, Peng Ding, Jasjeet S. Sekhon
The paper investigates regression when outcomes are observed only after passing through selection filters that depend on both covariates and outcomes, a common issue in fields such as clinical trials, labor markets, and auctions. It provides a complete characterization of the minimal assumptions on the functional forms of selection processes that allow regression to remain possible, and shows that the regression function can sometimes be identified even when the selection filter itself cannot. Under stronger identification conditions, the authors also deliver finite‑sample estimation guarantees, explicit convergence rates, and oracle‑efficient algorithms, offering the first general‑purpose estimation method for this broad class of selection problems.
By Vikram Kher, Jane H. Lee, Anay Mehrotra, Manolis Zampetakis
arXiv:2608.22597v1 Announce Type: new
Abstract: Subsampling is effective in tackling computational challenges for massive data with rare events. Overly aggressive subsampling may adversely affect est...
By Jing Wang, HaiYing Wang, Qiang Zhang, Hao Helen Zhang
arXiv:2306. 14851v5 Announce Type: replace-cross Abstract: Given a high-dimensional covariate matrix and a response vector, ridge-regularized sparse linear regression selects a subset of features that explains the relationship between covariates and the response in an interpretable manner.
By Ryan Cory-Wright, Andr\'es G\'omez