Exact results on high-dimensional linear regression via statistical physics

Autor:	Mozeika, Alexander, Sheikh, Mansoor, Aguirre-Lopez, Fabian, Antenucci, Fabrizio, Coolen, Anthony CC
Rok vydání:	2020
Předmět:	Mathematics - Statistics Theory Condensed Matter - Disordered Systems and Neural Networks
Zdroj:	Phys. Rev. E 103, 042142 (2021)
Druh dokumentu:	Working Paper
DOI:	10.1103/PhysRevE.103.042142
Popis:	It is clear that conventional statistical inference protocols need to be revised to deal correctly with the high-dimensional data that are now common. Most recent studies aimed at achieving this revision rely on powerful approximation techniques, that call for rigorous results against which they can be tested. In this context, the simplest case of high-dimensional linear regression has acquired significant new relevance and attention. In this paper we use the statistical physics perspective on inference to derive a number of new exact results for linear regression in the high-dimensional regime. Comment: Most recent version accepted for publication in Physical Review E
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2009.13229 Zobrazit plný text záznamu View this record from Arxiv