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001			21347946
005			20241204121046.0
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007			cr |||||||||||
008			191215s2020 nju ob 001 0 eng
010			_a 2019048293
020			_a9780691200316
020			_z9780691197296
040			_aDLC _beng _cDLC _erda _dDLC
042			_apcc
050	0	0	_aQA276 _b.J83 2020
082	0	0	_a519.5/4 _223
100	1		_aJuditsky, Anatoli, _d1962- _eauthor. _927350
245	1	0	_aStatistical inference via convex optimization / _c by Anatoli Juditsky, & Arkadi Nemirovski.
260			_aNew Jersey: _bPrinceton University Press, _c2020
300			_axv,631p.: _c26 cm
490	0		_aPrinceton series in applied mathematics
504			_aIncludes bibliographical references and index.
505	0		_aOn 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"--
650		0	_aMathematical statistics. _9890
650		0	_aMathematical optimization. _927351
650		0	_aConvex functions. _927352
700	1		_aNemirovskiĭ, A. S. _q(Arkadiĭ Semenovich), _eauthor. _927353
776	0	8	_iPrint version: _aJuditsky, Anatoli, 1962- _tStatistical inference via convex optimization _dPrinceton : Princeton University Press, [2020] _z9780691197296 _w(DLC) 2019048292
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cBK _hQA276 _kQA276 _m.J83 2020
999			_c9417 _d9417