High Dimensional Data Analysis With Low Dimensional Models
Download High Dimensional Data Analysis With Low Dimensional Models PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get High Dimensional Data Analysis With Low Dimensional Models book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
High-Dimensional Data Analysis with Low-Dimensional Models
Author: John Wright
language: en
Publisher: Cambridge University Press
Release Date: 2022-01-13
Connects fundamental mathematical theory with real-world problems, through efficient and scalable optimization algorithms.
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. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection. Highlighting cutting-edge research and casting light on future research directions, the contributions will benefit graduate students and researchers in computational biology, statistics and the machine learning community.
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.