Cantitate/Preț
Produs
Total produse
Vezi coșul
Statistics for High-Dimensional Data: Methods, Theory and Applications de Peter Bühlmann

Statistics for High-Dimensional Data: Methods, Theory and Applications: Springer Series in Statistics

Autor Peter Bühlmann, Sara Van de Geer
en Limba Engleză Paperback – 3 aug 2013
Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections.
A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 64089 lei  6-8 săpt.
  Springer Berlin, Heidelberg – 3 aug 2013 64089 lei  6-8 săpt.
Hardback (1) 88872 lei  6-8 săpt.
  Springer Berlin, Heidelberg – 8 iun 2011 88872 lei  6-8 săpt.

Din seria Springer Series in Statistics

Preț: 64089 lei

Preț vechi: 75398 lei
-15% Nou

Puncte Express: 961
Preț estimativ în valută:
12265€ • 12740$ • 10188£

Carte tipărită la comandă

Livrare economică 01-15 februarie 25

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783642268571
ISBN-10: 3642268579
Pagini: 576
Ilustrații: XVIII, 558 p.
Dimensiuni: 155 x 235 x 30 mm
Greutate: 0.8 kg
Ediția:2011
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Springer Series in Statistics

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Graduate

Cuprins

Introduction.- Lasso for linear models.- Generalized linear models and the Lasso.- The group Lasso.- Additive models and many smooth univariate functions.- Theory for the Lasso.- Variable selection with the Lasso.- Theory for l1/l2-penalty procedures.- Non-convex loss functions and l1-regularization.- Stable solutions.- P-values for linear models and beyond.- Boosting and greedy algorithms.- Graphical modeling.- Probability and moment inequalities.- Author Index.- Index.- References.- Problems at the end of each chapter.

Recenzii

From the reviews:
“This book is a complete study of ℓ1-penalization based statistical methods for high-dimensional data … . Definitely, this book is useful. … its strong level in mathematics makes it more suitable to researchers and graduate students who already have a strong background in statistics. … it gives the state-of-the-art of the theory, and therefore can be used for an advanced course on the topic. … the last part of the book is an exciting introduction to new research perspectives provided by ℓ1-penalized methods.” (Pierre Alquier, Mathematical Reviews, Issue 2012 e)
“All Classical Statisticians interested in the very popular but a bit old methodologies like the Lasso (Tibshirani, 1996), its modifications like adaptive Lasso (Zou, 2006), and their theory, computational algorithms, applications to bioinformatics and other high dimensional applications. All such researchers would find this book worth buying. It is written by two outstanding theoreticians with flair for clear writing and excellent applications. … theory depends a lot on new concentration inequalities coming from the French probabilists. The book has good collection of these, with proofs.” (Jayanta K. Ghosh, International Statistical Review, Vol. 80 (3), 2012)

Notă biografică

Peter Bühlmann is Professor of Statistics at ETH Zürich. His main research areas are high-dimensional statistical inference, machine learning, graphical modeling, nonparametric methods, and statistical modeling in the life sciences. He is currently editor of the Annals of Statistics. He was awarded a Medallion lecture by the Institute of Mathematical Statistics in 2009 and read a paper to the Royal Statistical Society in 2010.
Sara van de Geer has been a full professor at the ETH in Zürich since 2005. Her main areas of research are empirical process theory, statistical learning theory, and nonparametric and high-dimensional statistics. She is an associate editor of Probability Theory and Related Fields, The Scandinavian Journal of Statistics and Statistical Surveys and a member of the Swiss National Science Foundation and correspondent of the Dutch Royal Academy of Sciences.
She received the IMS medal in 2003 and the ISI award in 2005, and was an invited speaker at the International Conference of Mathematicians in 2010.


Textul de pe ultima copertă

Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections.
A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.

Caracteristici

Contains the fundamentals of the recent research in a very timely area Gives an overview of the area and adds many new insights There is a unique mix of methodology, theory, algorithms and applications The number of recent papers on the topic is huge Is a welcome consolidation Is an essential for the further development of theory and methods