ISBN:
9781482237955
,
1482237954
,
1322629536
,
9781322629537
,
042917389X
,
9780429173899
,
9781482237948
Sprache:
Englisch
Seiten:
1 online resource (270 pages)
Ausgabe:
1st edition
Serie:
Monographs on statistics & applied probability 139
Paralleltitel:
Erscheint auch als
Schlagwort(e):
Dimensional analysis
;
Multivariate analysis
;
Big data
;
Statistics
;
Electronic books ; local
;
Analyse dimensionnelle
;
Analyse multivariée
;
Données volumineuses
;
Statistique
;
statistics
;
MATHEMATICS ; Applied
;
MATHEMATICS ; Probability & Statistics ; General
;
Big data
;
Dimensional analysis
;
Multivariate analysis
;
Statistics
;
Boosting
;
Datenanalyse
;
Hochdimensionale Daten
;
Inferenzstatistik
;
Lasso-Methode
;
Mathematische Modellierung
;
Statistik
Kurzfassung:
Ever-greater computing technologies have given rise to an exponentially growing volume of data. Today massive data sets (with potentially thousands of variables) play an important role in almost every branch of modern human activity, including networks, finance, and genetics. However, analyzing such data has presented a challenge for statisticians and data analysts and has required the development of new statistical methods capable of separating the signal from the noise. Introduction to High-Dimensional Statistics is a concise guide to state-of-the-art models, techniques, and approaches for handling high-dimensional data. The book is intended to expose the reader to the key concepts and ideas in the most simple settings possible while avoiding unnecessary technicalities. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this highly accessible text: Describes the challenges related to the analysis of high-dimensional data Covers cutting-edge statistical methods including model selection, sparsity and the lasso, aggregation, and learning theory Provides detailed exercises at the end of every chapter with collaborative solutions on a wikisite Illustrates concepts with simple but clear practical examples Introduction to High-Dimensional Statistics is suitable for graduate students and researchers interested in discovering modern statistics for massive data. It can be used as a graduate text or for self-study.
Anmerkung:
Online resource; Title from title page (viewed December 17, 2014)
,
Mode of access: World Wide Web.
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