"[This book] succeeds singularly at providing a structured introduction to this active field of research. … it is arguably the most accessible overview yet published of the mathematical ideas and principles that one needs to master to enter the field of high-dimensional statistics. … recommended to anyone interested in the main results of current research in high-dimensional statistics as well as anyone interested in acquiring the core mathematical skills to enter this area of research."
―Journal of the American Statistical Association

Introduction to High-Dimensional Statistics, Second Edition preserves the philosophy of the first edition: to be a concise guide for students and researchers discovering the area and interested in the mathematics involved. The main concepts and ideas are presented in simple settings, avoiding thereby unessential technicalities. High-dimensional statistics is a fast-evolving field, and much progress has been made on a large variety of topics, providing new insights and methods. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this new edition:

Offers revised chapters from the previous edition, with the inclusion of many additional materials on some important topics, including compress sensing, estimation with convex constraints, the slope estimator, simultaneously low-rank and row-sparse linear regression, or aggregation of a continuous set of estimators.

Introduces three new chapters on iterative algorithms, clustering, and minimax lower bounds.

Provides enhanced appendices, minimax lower-bounds mainly with the addition of the Davis-Kahan perturbation bound and of two simple versions of the Hanson-Wright concentration inequality.

Covers cutting-edge statistical methods including model selection, sparsity and the Lasso, iterative hard thresholding, aggregation, support vector machines, and learning theory.

Provides detailed exercises at the end of every chapter with collaborative solutions on a wiki site.

Illustrates concepts with simple but clear practical examples.

Informazioni sull?autore

Christophe Giraud was a student of the �cole Normale Sup�rieure de Paris, and he received a Ph.D in probability theory from the University Paris 6. He was assistant professor at the University of Nice from 2002 to 2008. He has been associate professor at the �cole Polytechnique since 2008 and professor at Paris Sud University (Orsay) since 2012. His current research focuses mainly on the statistical theory of high-dimensional data analysis and its applications to life sciences.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Descrizione libreria

Visita la pagina della libreria

Informazioni sull?azienda del venditore

Edward Bowditch Ltd
Exstowe, Exton, Exeter, EX3 0PP, United Kingdom

Condizioni di vendita e spedizione

Condizioni di vendita

Legal entity name: Edward Bowditch Ltd
Legal entity form: Limited company
Business correspondence address: Exstowe, Exton, Exeter, EX3 0PP
Company registration number: 04916632
VAT registration: GB834241546
Authorised representative: Mr. E. Bowditch

Diritto di recesso

Condizioni di spedizione

Orders usually dispatched within two working days.

Tariffe di spedizione da Regno Unito a U.S.A.

Tariffe di spedizione da Regno Unito a U.S.A.
Quantit� dell?ordine	Da 7 a 18 giorni lavorativi	Da 2 a 5 giorni lavorativi
Primo articolo	EUR 28.63	EUR 28.63

I tempi di consegna sono stabiliti dai venditori e variano in base al corriere e al paese. Gli ordini che devono attraversare una dogana possono subire ritardi e spetta agli acquirenti pagare eventuali tariffe o dazi associati. I venditori possono contattarti in merito ad addebiti aggiuntivi dovuti a eventuali maggiorazioni dei costi di spedizione dei tuoi articoli.