Statistical Analysis for High-Dimensional Data

The Abel Symposium 2014

Nonfiction, Science & Nature, Mathematics, Counting & Numeration, Statistics
Cover of the book Statistical Analysis for High-Dimensional Data by , Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: ISBN: 9783319270999
Publisher: Springer International Publishing Publication: February 16, 2016
Imprint: Springer Language: English
Author:
ISBN: 9783319270999
Publisher: Springer International Publishing
Publication: February 16, 2016
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer International Publishing

Cover of the book Biracial Families by
Cover of the book Topology by
Cover of the book Modelling and Simulation for Autonomous Systems by
Cover of the book Introduction to Polyphasic Dispersed Systems Theory by
Cover of the book A Comet of the Enlightenment by
Cover of the book From Variability Tolerance to Approximate Computing in Parallel Integrated Architectures and Accelerators by
Cover of the book Designing with Xilinx® FPGAs by
Cover of the book Smart City Networks by
Cover of the book Elder Abuse by
Cover of the book Progress in the Chemistry of Organic Natural Products 108 by
Cover of the book Numbers and Computers by
Cover of the book Temporal Bone Cancer by
Cover of the book Performance and Civic Engagement by
Cover of the book Probability for Statisticians by
Cover of the book Disordered Vertebral and Rib Morphology in Pudgy Mice by
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy