Generalized Linear Models in Distributed Environments: a Framework for Optimal Subset Selection

Le résumé fourni par la source

Modern data analysis often involves challenges from distributed and high-dimensional data, especially in fields such as finance and genomics.While distributed statistical methods address fragmented datasets and subset selection tackles high-dimensionality, existing approaches lack an effective combination of these two aspects.This paper proposes a communication-efficient distributed algorithm for best-subset selection in generalized linear models.The method extends a state-of-the-art centralized best-subset selection approach to a distributed framework, enabling scalable variable selection while optimizing computational efficiency and communication cost.Theoretical guarantees are established, including statistical consistency and convergence rates.Simulation studies and real-world data analyses validate the method's superior performance in terms of predictive accuracy and computational efficiency, demonstrating its practical relevance for modern data challenges.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.