Statistics for High-Dimensional Data

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.

שנה

2011

שפה

English

סדרה

Springer Series in Statistics

הוצאה

Springer-Verlag Berlin Heidelberg

מהדורות

2

עמודים

556

ISBN

9783642201912

מקור הנתונים: Wikidata