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| 049 | _aMAIN | ||
| 100 | 1 |
_aJuditsky, Anatoli, _d1962- _eauthor. _965478 |
|
| 245 | 1 | 0 |
_aStatistical inference via convex optimization / _cAnatoli Juditsky, Arkadi Nemirovski. |
| 264 | 1 |
_aPrinceton, New Jersey : _bPrinceton University Press, _c[2020] |
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| 264 | 4 | _c�2020 | |
| 300 |
_a1 online resource (xx, 631 pages) : _billustrations |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bn _2rdamedia |
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| 338 |
_aonline resource _bnc _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 | _aPrinceton series in applied mathematics | |
| 504 | _aIncludes bibliographical references and index. | ||
| 505 | 0 | _aOn computational tractability -- Sparse recovery via �b1s 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"-- _cProvided by publisher. |
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| 588 | 0 | _aDescription based on online resource, title from digital title page (viewed on February 12, 2021). | |
| 590 |
_aIEEE _bIEEE Xplore Princeton University Press eBooks Library |
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| 650 | 0 |
_aMathematical statistics. _99597 |
|
| 650 | 0 |
_aMathematical optimization. _94112 |
|
| 650 | 0 |
_aConvex functions. _965479 |
|
| 650 | 6 |
_aOptimisation math�ematique. _963677 |
|
| 650 | 6 |
_aFonctions convexes. _965480 |
|
| 650 | 7 |
_aMATHEMATICS _xOptimization. _2bisacsh _963680 |
|
| 650 | 7 |
_aConvex functions. _2fast _0(OCoLC)fst00877260 _965479 |
|
| 650 | 7 |
_aMathematical optimization. _2fast _0(OCoLC)fst01012099 _94112 |
|
| 650 | 7 |
_aMathematical statistics. _2fast _0(OCoLC)fst01012127 _99597 |
|
| 655 | 0 |
_aElectronic books. _93294 |
|
| 655 | 4 |
_aElectronic books. _93294 |
|
| 700 | 1 |
_aNemirovski�i, A. S. _q(Arkadi�i Semenovich), _eauthor. _963734 |
|
| 776 | 0 | 8 |
_iPrint version: _aJuditsky, Anatoli, 1962- _tStatistical inference via convex optimization. _dPrinceton, New Jersey : Princeton University Press, [2020] _z9780691197296 _w(DLC) 2019048292 _w(OCoLC)1119533070 |
| 830 | 0 |
_aPrinceton series in applied mathematics. _965481 |
|
| 856 | 4 | 0 | _uhttps://ieeexplore.ieee.org/servlet/opac?bknumber=9453317 |
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