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Concentration of Maxima and Fundamental Limits in High-Dimensional Testing and Inference

BuchKartoniert, Paperback
140 Seiten
Englisch
Springererschienen am08.09.20211st ed. 2021
This book provides a unified exposition of some fundamental theoretical problems in high-dimensional statistics. Based on a surprising connection to a concentration of maxima probabilistic phenomenon, the authors obtain a complete characterization of the exact support recovery problem for thresholding estimators under dependent errors.mehr
Verfügbare Formate
BuchKartoniert, Paperback
EUR69,54
E-BookPDF1 - PDF WatermarkE-Book
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Produkt

KlappentextThis book provides a unified exposition of some fundamental theoretical problems in high-dimensional statistics. Based on a surprising connection to a concentration of maxima probabilistic phenomenon, the authors obtain a complete characterization of the exact support recovery problem for thresholding estimators under dependent errors.
Zusammenfassung
Provides a unified exposition of fundamental problems in high-dimensional statistics

Tackles canonical problems of detection and support estimation for sparse signals observed with noise

Gives an application to statistical genetics
Details
ISBN/GTIN978-3-030-80963-8
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2021
Erscheinungsdatum08.09.2021
Auflage1st ed. 2021
ReiheSpringerBriefs in Probability and Mathematical Statistics
Seiten140 Seiten
SpracheEnglisch
IllustrationenXIII, 140 p. 12 illus., 2 illus. in color.
Artikel-Nr.49902196

Inhalt/Kritik

Inhaltsverzeichnis
Chapter 1 Introduction and Guiding Examples.- Chapter 2 Risks, Procedures, and Error Models.- Chapter 3 A Panorama of Phase Transitions.- Chapter 4 Exact Support Recovery Under Dependence.- Chapter 5 Bayes and Minimax Optimality.- Chapter 6 Uniform Relative Stability for Gaussian Array.- Chapter 7 Fundamental Statistical Limits in Genome-wide Association Studies.- References.- Additional proofs.- Exact support recovery in non AGG models.mehr

Autor

Zheng Gao graduated with a PhD in Statistics from the University of Michigan in 2020. His research focuses on large-scale multiple testing problems and real-time anomaly detection on high-dimensional data streams.
Stilian Stoev is a Full Professor of Statistics at the University of Michigan, Ann Arbor. His research involves topics in applied probability, statistics and their applications to insurance and computer networks. Most recently, he has been working on extreme value theory.
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