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Titlebook: Concentration of Maxima and Fundamental Limits in High-Dimensional Testing and Inference; Zheng Gao,Stilian Stoev Book 2021 The Author(s),

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發(fā)表于 2025-3-21 16:05:50 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Concentration of Maxima and Fundamental Limits in High-Dimensional Testing and Inference
編輯Zheng Gao,Stilian Stoev
視頻videohttp://file.papertrans.cn/235/234858/234858.mp4
概述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 appl
叢書名稱SpringerBriefs in Probability and Mathematical Statistics
圖書封面Titlebook: Concentration of Maxima and Fundamental Limits in High-Dimensional Testing and Inference;  Zheng Gao,Stilian Stoev Book 2021 The Author(s),
描述.This book provides a unified exposition of some fundamental theoretical problems in high-dimensional statistics. It specifically?considers the canonical problems of detection and support estimation for sparse signals observed with noise. Novel phase-transition results are obtained for the signal support estimation problem under a variety of statistical risks. 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.?.
出版日期Book 2021
關(guān)鍵詞High-dimensional statistics; Concentration of maxima; Testing problems; Support recovery problems; Stati
版次1
doihttps://doi.org/10.1007/978-3-030-80964-5
isbn_softcover978-3-030-80963-8
isbn_ebook978-3-030-80964-5Series ISSN 2365-4333 Series E-ISSN 2365-4341
issn_series 2365-4333
copyrightThe Author(s), under exclusive license to Springer Nature Switzerland AG 2021
The information of publication is updating

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Risks, Procedures, and Error Models,ional setting. First, a general notion of statistical risk is formulated and used to provide a unified treatment of a variety of signal detection and support estimation problems considered in the literature. The relationship between various choices of risk functionals and their implications on the a
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Bayes and Minimax Optimality, likelihood ratio thresholding procedures are always Bayes optimal. Consequently, the Oracle threshold estimation procedures are optimal for the class of log-concave error models and, otherwise, they are typically sub-optimal. These results lead to a slew of asymptotic minimax characterizations of t
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Uniform Relative Stability for Gaussian Arrays, arrays known as the uniform relative stability (URS) property. This property holds for a wide range of models. The purpose of this chapter is to provide a complete characterization of URS for Gaussian triangular arrays. The main result shows that the Gaussian arrays are URS if and only if they are
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,Fundamental Statistical Limits in?Genome-Wide Association Studies,WAS) aim to discover the set of gene variants associated with a disease. In the simple case of marginal association studies, one typically considers 2-by-2 contingency tables and computes non-central chi-square-type statistics (one for each genetic location), where the non-centrality parameter plays
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Time-Varying Discrete Linear Systems.?2 yields a variety of phase-transition results, many of which are new. The optimality and sub-optimality of some popular support estimation procedures are also established. The chapter provides a concise and yet complete account of the phase transitions in the context of sparse signal detection and support estimation in high dimensions.
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