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

Apraxia

Time-Varying Discrete Linear Systemsional 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

Sleep-Paralysis

Time-Varying Discrete Linear Systemsroblems for sparse signals. In order to emphasize ideas, the focus is on the simple case of a high-dimensional sparse signal observed with additive independent Gaussian errors. The classic phase-transition result for the signal detection problem obtained first by Yuri Izmailovich Ingster as well as

Morbid

酷热

https://doi.org/10.1007/978-3-030-70944-0 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

confederacy

NOVA

Time-Varying Maximum Capacity Path Problems,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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Concentration of Maxima and Fundamental Limits in High-Dimensional Testing and Inference978-3-030-80964-5Series ISSN 2365-4333 Series E-ISSN 2365-4341

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充满人

https://doi.org/10.1007/978-3-030-80964-5High-dimensional statistics; Concentration of maxima; Testing problems; Support recovery problems; Stati

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