High-Dimensional Statistics : A Non-Asymptotic Viewpoint Hardback
by Martin J. (University of California, Berkeley) Wainwright
Part of the Cambridge Series in Statistical and Probabilistic Mathematics series
Hardback
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Description
Recent 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.
Information
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In Stock - low on stock, only 1 copy remainingFree UK DeliveryEstimated delivery 2-3 working days
- Format:Hardback
- Pages:568 pages, Worked examples or Exercises; 1 Tables, black and white; 25 Halftones, black and white; 2
- Publisher:Cambridge University Press
- Publication Date:21/02/2019
- Category:
- ISBN:9781108498029
Information
-
In Stock - low on stock, only 1 copy remainingFree UK DeliveryEstimated delivery 2-3 working days
- Format:Hardback
- Pages:568 pages, Worked examples or Exercises; 1 Tables, black and white; 25 Halftones, black and white; 2
- Publisher:Cambridge University Press
- Publication Date:21/02/2019
- Category:
- ISBN:9781108498029