Statistics for High-Dimensional Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Statistics for High-Dimensional Data write by Peter Bühlmann. This book was released on 2011-06-08. Statistics for High-Dimensional Data available in PDF, EPUB and Kindle. 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.
Statistical Analysis for High-Dimensional Data
Statistical Analysis for High-Dimensional Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Statistical Analysis for High-Dimensional Data write by Arnoldo Frigessi. This book was released on 2016-02-16. Statistical Analysis for High-Dimensional Data available in PDF, EPUB and Kindle. 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.
High-Dimensional Statistics
High-Dimensional Statistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook High-Dimensional Statistics write by Martin J. Wainwright. This book was released on 2019-02-21. High-Dimensional Statistics available in PDF, EPUB and Kindle. A coherent introductory text from a groundbreaking researcher, focusing on clarity and motivation to build intuition and understanding.
High-Dimensional Data Analysis with Low-Dimensional Models
High-Dimensional Data Analysis with Low-Dimensional Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook High-Dimensional Data Analysis with Low-Dimensional Models write by John Wright. This book was released on 2022-01-13. High-Dimensional Data Analysis with Low-Dimensional Models available in PDF, EPUB and Kindle. Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès.
Analysis of Multivariate and High-Dimensional Data
Analysis of Multivariate and High-Dimensional Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Analysis of Multivariate and High-Dimensional Data write by Inge Koch. This book was released on 2014. Analysis of Multivariate and High-Dimensional Data available in PDF, EPUB and Kindle. This modern approach integrates classical and contemporary methods, fusing theory and practice and bridging the gap to statistical learning.
