Tootfinder

Modification of a Numerical Method Using FIR Filters in a Time-dependent SIR Model for COVID-19
Felipe Rog\'erio Pimentel, Rafael Gustavo Alves
https://arxiv.org/abs/2506.21739

Modification of a Numerical Method Using FIR Filters in a Time-dependent SIR Model for COVID-19
Authors Yi-Cheng Chen, Ping-En Lu, Cheng-Shang Chang, and Tzu-Hsuan Liu use the Finite Impulse Response (FIR) linear system filtering method to track and predict the number of people infected and recovered from COVID-19, in a pandemic context in which there was still no vaccine and the only way to avoid contagion was isolation. To estimate the coefficients of these FIR filters, Chen et al. used machine learning methods through a classical optimization problem with regularization (ridge regressi…

GRASP: Grouped Regression with Adaptive Shrinkage Priors
Shu Yu Tew, Daniel F. Schmidt, Mario Boley
https://arxiv.org/abs/2506.18092 https://

GRASP: Grouped Regression with Adaptive Shrinkage Priors
We introduce GRASP, a simple Bayesian framework for regression with grouped predictors, built on the normal beta prime (NBP) prior. The NBP prior is an adaptive generalization of the horseshoe prior with tunable hyperparameters that control tail behavior, enabling a flexible range of sparsity, from strong shrinkage to ridge-like regularization. Unlike prior work that introduced the group inverse-gamma gamma (GIGG) prior by decomposing the NBP prior into structured hierarchies, we show that dire…

Asymptotic Consistency and Generalization in Hybrid Models of Regularized Selection and Nonlinear Learning
Luciano Ribeiro Galv\~ao, Rafael de Andrade Mora
https://arxiv.org/abs/2508.07754

Asymptotic Consistency and Generalization in Hybrid Models of Regularized Selection and Nonlinear Learning
This study explores how different types of supervised models perform in the task of predicting and selecting relevant variables in high-dimensional contexts, especially when the data is very noisy. We analyzed three approaches: regularized models (such as Lasso, Ridge, and Elastic Net), black-box models (such as Random Forest, XGBoost, LightGBM, CatBoost, and H2O GBM), and hybrid models that combine both approaches: regularization with nonlinear algorithms. Based on simulations inspired by the …