Statistics for High-Dimensional Data

Statistics for High-Dimensional Data by Peter BГјhlmann Download PDF EPUB FB2

Statistics for High-Dimensional Data: Methods, Theory and Applications (Springer Series in Statistics) th Edition. Statistics for High-Dimensional Data: Methods, Theory and Applications (Springer Series in Statistics) th Edition. Find all the books, read about the author, and by:   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/5(5).

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 Cited by: Methods, Theory and Applications.

Usually dispatched within 3 to 5 business days. Usually dispatched within 3 to 5 business days. Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book intends to examine important issues arising from high-dimensional data analysis to explore key ideas for statistical inference and is structured around topics on multiple hypothesis testing, feature selection, regression, classification, dimension reduction, as well as applications in survival analysis and biomedical Format: Hardcover.

'Non-asymptotic, high-dimensional theory is critical for modern statistics and machine learning. This book is unique in providing a crystal clear, complete and unified treatment of the area. With topics ranging from concentration of measure to graphical models, the author weaves together probability theory and its applications to by: Statistics for Statistics for High-Dimensional Data book 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 Brand: Springer.

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.

I would recommend this book for those interested in (high-dimensional) statistics, randomized numerical linear algebra, and electrical engineering (particularly, signal processing).

As I'm coming to realize, the "concentration of measure" and “deviation inequality” toolbox is essential to these by: The forthcoming book [19] presents a panorama of mathematical data science, and it particularly focuses on applications in computer science.

Both these books are accessible to gradu-ate and advanced undergraduate students. The lecture notes [] are pitched for graduate students and present more theoretical material in high-dimensional probability.

van Wieringen W and Peeters C () Ridge estimation of inverse covariance matrices from high-dimensional data, Computational Statistics & Data Analysis, C, (.

These lecture notes were written for the courseHigh Dimensional Statistics at MIT. They build on a set of notes that was prepared at Prince-ton University in that was modi ed (and hopefully improved) over the years.

Over the past decade, statistics have undergone drastic changes with theFile Size: 1MB. The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data.

Statistics for high-dimensional data: Introduction, and the Lasso for linear models Peter Buhlmann and Sara van de Geer¨ Seminar fur Statistik, ETH Z¨ ¨urich May High-dimensional data Riboflavin production with Bacillus Subtilis (in collaboration with DSM (Switzerland)). 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 Reviews: 1.

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. Fashionable statistics presents with big and complicated data models, and consequently with fashions containing quite a few parameters.

This book presents an in depth account of simply these days developed approaches, along with the Lasso and variations of it for quite a few fashions, boosting methods, undirected graphical modeling, and procedures controlling false constructive decisions.A. Statistics for High-Dimensional Data: Methods, Theory and Applications - Ebook written by Peter Bühlmann, Sara van de Geer.

Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Statistics for High-Dimensional Data: Methods, Theory and Applications.

In statistical theory, the field of high-dimensional statistics studies data whose dimension is larger than dimensions considered in classical multivariate analysis. High-dimensional statistics relies on the theory of random vectors.

In many applications, the dimension of the data. The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data by:.

I am Professor of Mathematics at the University of California, Irvine working in high-dimensional probability theory and its applications. I study probabilistic structures that appear across mathematics and data sciences, in particular random matrix theory, geometric functional analysis, convex and discrete geometry, high-dimensional statistics, information theory, learning theory, signal.: Statistics for High-Dimensional Data: Methods, Theory and Applications () by Bühlmann, Peter; Van De Geer, Sara and a great selection of similar New, Used and Collectible Books available now at great prices/5(5).

This book intends to examine important issues arising from high-dimensional data analysis to explore key ideas for statistical inference and prediction. It is structured around topics on multiple hypothesis testing, feature selection, regression, classification, dimension reduction, as well as applications in survival analysis and biomedical.