000 | a | ||
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999 |
_c32210 _d32210 |
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008 | 231101b xxu||||| |||| 00| 0 eng d | ||
020 | _a9781108498029 | ||
082 |
_a519.5 _bWAI |
||
100 | _aWainwright, Martin J. | ||
245 | _aHigh-dimensional statistics : a non-asymptotic viewpoint | ||
260 |
_bCambridge University Press, _c2019 _aCambridge : |
||
300 |
_axvii, 552 p. ; _bill., _c26 cm. |
||
365 |
_b64.99 _cGBP _d107.60 |
||
490 | _aCambridge series in statistical and probabilistic mathematics | ||
504 | _aIncludes bibliographical references and indexes. | ||
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 | _aBig data | ||
650 | _aMathematical statistics | ||
650 | _aMetric entropy | ||
650 | _aFrobenius norm | ||
650 | _aHilbert space | ||
650 | _aKullback–Leibler divergence | ||
650 | _aLipschitz functions | ||
942 |
_2ddc _cBK |