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Statistical inference via convex optimization /
"This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Princeton, New Jersey :
Princeton University Press,
[2020]
|
| Colección: | Princeton series in applied mathematics.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Juditsky, Anatoli, |d 1962- |e author. | |
| 245 | 1 | 0 | |a Statistical inference via convex optimization / |c Anatoli Juditsky, Arkadi Nemirovski. |
| 264 | 1 | |a Princeton, New Jersey : |b Princeton University Press, |c [2020] | |
| 264 | 4 | |c ©2020 | |
| 300 | |a 1 online resource (xx, 631 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b n |2 rdamedia | ||
| 338 | |a online resource |b nc |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Princeton series in applied mathematics | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a On computational tractability -- Sparse recovery via ℓ₁ minimization -- Hypothesis testing -- From hypothesis testing to estimating functionals -- Signal recovery by linear estimation -- Signal recovery beyond linear estimates -- Solutions to selected exercises. | |
| 520 | |a "This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory can be used to devise and analyze near-optimal statistical inferences. Statistical Inference via Convex Optimization is an essential resource for optimization specialists who are new to statistics and its applications, and for data scientists who want to improve their optimization methods. Juditsky and Nemirovski provide the first systematic treatment of the statistical techniques that have arisen from advances in the theory of optimization. They focus on four well-known statistical problems--sparse recovery, hypothesis testing, and recovery from indirect observations of both signals and functions of signals--demonstrating how they can be solved more efficiently as convex optimization problems. The emphasis throughout is on achieving the best possible statistical performance. The construction of inference routines and the quantification of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical approaches. In addition to being computation-friendly, the methods described in this book enable practitioners to handle numerous situations too difficult for closed analytical form analysis, such as composite hypothesis testing and signal recovery in inverse problems. Statistical Inference via Convex Optimization features exercises with solutions along with extensive appendixes, making it ideal for use as a graduate text"-- |c Provided by publisher. | ||
| 588 | 0 | |a Description based on online resource, title from digital title page (viewed on February 12, 2021). | |
| 590 | |a JSTOR |b Books at JSTOR Demand Driven Acquisitions (DDA) | ||
| 590 | |a JSTOR |b Books at JSTOR Evidence Based Acquisitions | ||
| 590 | |a JSTOR |b Books at JSTOR All Purchased | ||
| 650 | 0 | |a Mathematical statistics. | |
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Convex functions. | |
| 650 | 6 | |a Optimisation mathématique. | |
| 650 | 6 | |a Fonctions convexes. | |
| 650 | 7 | |a MATHEMATICS |x Optimization. |2 bisacsh | |
| 650 | 7 | |a Convex functions. |2 fast |0 (OCoLC)fst00877260 | |
| 650 | 7 | |a Mathematical optimization. |2 fast |0 (OCoLC)fst01012099 | |
| 650 | 7 | |a Mathematical statistics. |2 fast |0 (OCoLC)fst01012127 | |
| 653 | |a All of Nonparametric Statistics. | ||
| 653 | |a Asymptotic Methods in Statistical Decision Theory. | ||
| 653 | |a Dantzig selector. | ||
| 653 | |a Gaussian observations. | ||
| 653 | |a Has'minskii. | ||
| 653 | |a Hellinger distance. | ||
| 653 | |a Ibragimov. | ||
| 653 | |a Introduction to Nonparametric Estimation. | ||
| 653 | |a Lagrange duality. | ||
| 653 | |a Le Cam. | ||
| 653 | |a N-convex function. | ||
| 653 | |a Statistical Estimation. | ||
| 653 | |a Tsybakov. | ||
| 653 | |a Wasserman. | ||
| 653 | |a bisection algorithm. | ||
| 653 | |a conic programming. | ||
| 653 | |a convex sets. | ||
| 653 | |a duality. | ||
| 653 | |a ell-1-norm minimization. | ||
| 653 | |a estimating functions. | ||
| 653 | |a lasso selector. | ||
| 653 | |a minimization. | ||
| 653 | |a saddle points. | ||
| 653 | |a signal plus noise. | ||
| 653 | |a signal-to-noise. | ||
| 653 | |a unobserved signal. | ||
| 653 | |a variable selection. | ||
| 700 | 1 | |a Nemirovskiĭ, A. S. |q (Arkadiĭ Semenovich), |e author. | |
| 776 | 0 | 8 | |i Print version: |a Juditsky, Anatoli, 1962- |t Statistical inference via convex optimization. |d Princeton, New Jersey : Princeton University Press, [2020] |z 9780691197296 |w (DLC) 2019048292 |w (OCoLC)1119533070 |
| 830 | 0 | |a Princeton series in applied mathematics. | |
| 856 | 4 | 0 | |u https://jstor.uam.elogim.com/stable/10.2307/j.ctvqsdxqd |z Texto completo |
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| 938 | |a EBSCOhost |b EBSC |n 2270814 | ||
| 938 | |a Askews and Holts Library Services |b ASKH |n AH37092829 | ||
| 994 | |a 92 |b IZTAP | ||