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 theory...
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| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Princeton, NJ
Princeton University Press
[2020]
|
| Schriftenreihe: | Princeton Series in Applied Mathematics
69 |
| Schlagworte: | |
| Online-Zugang: | FAB01 FAW01 FHA01 FHR01 FKE01 FLA01 TUM01 UBY01 UPA01 FCO01 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | Description based on online resource; title from PDF title page (publisher's Web site, viewed 24. Apr 2020) |
| Beschreibung: | 1 online resource (656 pages) 40 b/w illus |
| ISBN: | 9780691200316 |
| DOI: | 10.1515/9780691200316 |
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| 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 | ||
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| index_date | 2024-07-03T14:31:27Z |
| indexdate | 2024-07-10T08:51:49Z |
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| isbn | 9780691200316 |
| language | English |
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| record_format | marc |
| series2 | Princeton Series in Applied Mathematics |
| spelling | Juditsky, Anatoli 1962- Verfasser (DE-588)171869257 aut Statistical Inference via Convex Optimization Anatoli Juditsky, Arkadi Nemirovski Princeton, NJ Princeton University Press [2020] © 2020 1 online resource (656 pages) 40 b/w illus txt rdacontent c rdamedia cr rdacarrier Princeton Series in Applied Mathematics 69 Description based on online resource; title from PDF title page (publisher's Web site, viewed 24. Apr 2020) 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 In English All of Nonparametric Statistics Asymptotic Methods in Statistical Decision Theory Dantzig selector Gaussian observations Has'minskii Hellinger distance Ibragimov Introduction to Nonparametric Estimation Lagrange duality Le Cam N-convex function Statistical Estimation Tsybakov Wasserman bisection algorithm conic programming convex sets duality ell-1-norm minimization estimating functions lasso selector minimization saddle points signal plus noise signal-to-noise unobserved signal variable selection MATHEMATICS / Optimization bisacsh Nemirovskij, Arkadij S. 1947- (DE-588)12209395X aut Erscheint auch als Druck-Ausgabe 978-0-691-19729-6 https://doi.org/10.1515/9780691200316 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Juditsky, Anatoli 1962- Nemirovskij, Arkadij S. 1947- Statistical Inference via Convex Optimization All of Nonparametric Statistics Asymptotic Methods in Statistical Decision Theory Dantzig selector Gaussian observations Has'minskii Hellinger distance Ibragimov Introduction to Nonparametric Estimation Lagrange duality Le Cam N-convex function Statistical Estimation Tsybakov Wasserman bisection algorithm conic programming convex sets duality ell-1-norm minimization estimating functions lasso selector minimization saddle points signal plus noise signal-to-noise unobserved signal variable selection MATHEMATICS / Optimization bisacsh |
| title | Statistical Inference via Convex Optimization |
| title_auth | Statistical Inference via Convex Optimization |
| title_exact_search | Statistical Inference via Convex Optimization |
| title_exact_search_txtP | Statistical Inference via Convex Optimization |
| title_full | Statistical Inference via Convex Optimization Anatoli Juditsky, Arkadi Nemirovski |
| title_fullStr | Statistical Inference via Convex Optimization Anatoli Juditsky, Arkadi Nemirovski |
| title_full_unstemmed | Statistical Inference via Convex Optimization Anatoli Juditsky, Arkadi Nemirovski |
| title_short | Statistical Inference via Convex Optimization |
| title_sort | statistical inference via convex optimization |
| topic | All of Nonparametric Statistics Asymptotic Methods in Statistical Decision Theory Dantzig selector Gaussian observations Has'minskii Hellinger distance Ibragimov Introduction to Nonparametric Estimation Lagrange duality Le Cam N-convex function Statistical Estimation Tsybakov Wasserman bisection algorithm conic programming convex sets duality ell-1-norm minimization estimating functions lasso selector minimization saddle points signal plus noise signal-to-noise unobserved signal variable selection MATHEMATICS / Optimization bisacsh |
| topic_facet | All of Nonparametric Statistics Asymptotic Methods in Statistical Decision Theory Dantzig selector Gaussian observations Has'minskii Hellinger distance Ibragimov Introduction to Nonparametric Estimation Lagrange duality Le Cam N-convex function Statistical Estimation Tsybakov Wasserman bisection algorithm conic programming convex sets duality ell-1-norm minimization estimating functions lasso selector minimization saddle points signal plus noise signal-to-noise unobserved signal variable selection MATHEMATICS / Optimization |
| url | https://doi.org/10.1515/9780691200316 |
| work_keys_str_mv | AT juditskyanatoli statisticalinferenceviaconvexoptimization AT nemirovskijarkadijs statisticalinferenceviaconvexoptimization |