| 000 | 02412nam a2200361 i 4500 | ||
|---|---|---|---|
| 001 | CR9781108627771 | ||
| 003 | UkCbUP | ||
| 005 | 20240910160651.0 | ||
| 006 | m|||||o||d|||||||| | ||
| 007 | cr|||||||||||| | ||
| 008 | 180522s2019||||enk o ||1 0|eng|d | ||
| 020 | _a9781108627771 (ebook) | ||
| 020 | _z9781108498029 (hardback) | ||
| 040 |
_aUkCbUP _beng _erda _cUkCbUP |
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| 050 | 0 | 0 |
_aQA276.18 _b.W35 2019 |
| 082 | 0 | 0 |
_a519.5 _223 |
| 100 | 1 |
_aWainwright, Martin _q(Martin J.), _eauthor. |
|
| 245 | 1 | 0 |
_aHigh-dimensional statistics : _ba non-asymptotic viewpoint / _cMartin J. Wainwright. |
| 264 | 1 |
_aCambridge : _bCambridge University Press, _c2019. |
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| 300 |
_a1 online resource (xvii, 552 pages) : _bdigital, PDF file(s). |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 490 | 1 |
_aCambridge series in statistical and probabilistic mathematics ; _v48 |
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| 500 | _aTitle from publisher's bibliographic system (viewed on 13 Feb 2019). | ||
| 520 | _aRecent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices - as well as chapters devoted to in-depth exploration of particular model classes - including sparse linear models, matrix models with rank constraints, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and adapt modern statistical methods suited to large-scale data. | ||
| 650 | 0 |
_aMathematical statistics _vTextbooks. |
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| 650 | 0 | _aBig data. | |
| 776 | 0 | 8 |
_iPrint version: _z9781108498029 |
| 830 | 0 |
_aCambridge series on statistical and probabilistic mathematics ; _v48. |
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| 856 | 4 | 0 | _uhttps://www.cambridge.org/core/product/identifier/9781108627771/type/BOOK |
| 942 |
_2ddc _cEB |
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| 999 |
_c9586 _d9586 |
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