Statistics for High Dimensional Data

This book PDF is perfect for those who love Mathematics genre, written by Peter Bühlmann and published by Springer Science & Business Media which was released on 08 June 2011 with total hardcover pages 558. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Statistics for High Dimensional Data books below.

Statistics for High Dimensional Data
Author : Peter Bühlmann
File Size : 42,9 Mb
Publisher : Springer Science & Business Media
Language : English
Release Date : 08 June 2011
ISBN : 9783642201929
Pages : 558 pages
DOWNLOAD

Statistics for High Dimensional Data by Peter Bühlmann Book PDF Summary

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.

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.

DOWNLOAD
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.

DOWNLOAD
Introduction to High Dimensional Statistics

Praise for the first edition: "[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

DOWNLOAD
High Dimensional Statistics

A coherent introductory text from a groundbreaking researcher, focusing on clarity and motivation to build intuition and understanding.

DOWNLOAD
Statistical Analysis for High Dimensional Data

This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvågar, Lofoten, Norway, in May 2014. The focus of the symposium was on statistical and machine learning methodologies specifically developed for inference in “big data” situations, with particular reference to genomic applications.

DOWNLOAD
Fundamentals of High Dimensional Statistics

This textbook provides a step-by-step introduction to the tools and principles of high-dimensional statistics. Each chapter is complemented by numerous exercises, many of them with detailed solutions, and computer labs in R that convey valuable practical insights. The book covers the theory and practice of high-dimensional linear regression, graphical models,

DOWNLOAD
High Dimensional Probability

An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

DOWNLOAD
Analysis of Multivariate and High Dimensional Data

This modern approach integrates classical and contemporary methods, fusing theory and practice and bridging the gap to statistical learning.

DOWNLOAD