000			01775 a2200229 4500
999			_c53136 _d53136
008			201020b ||||| |||| 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. _aUK:
300			_axvii, 552 p. : ill. ; _bhb; _c26 cm.
365			_aGBP _b57.99
440			_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			_aMathematics
650			_aMathematical Statistics
650			_aBig Data
650			_aProbabilities
942			_2ddc _cTD