Statistical Analysis for High Dimensional Data

Statistical Analysis for High Dimensional Data
Author: Arnoldo Frigessi,Peter Bühlmann,Ingrid Glad,Mette Langaas,Sylvia Richardson,Marina Vannucci
Publsiher: Springer
Total Pages: 306
Release: 2016-02-16
Genre: Mathematics
ISBN: 9783319270999

Download Statistical Analysis for High Dimensional Data Book 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.

Statistics for High Dimensional Data

Statistics for High Dimensional Data
Author: Peter Bühlmann,Sara van de Geer
Publsiher: Springer Science & Business Media
Total Pages: 568
Release: 2011-06-08
Genre: Mathematics
ISBN: 9783642201929

Download Statistics for High Dimensional Data Book 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.

High Dimensional Data Analysis in Cancer Research

High Dimensional Data Analysis in Cancer Research
Author: Xiaochun Li,Ronghui Xu
Publsiher: Springer Science & Business Media
Total Pages: 164
Release: 2008-12-19
Genre: Medical
ISBN: 9780387697659

Download High Dimensional Data Analysis in Cancer Research Book in PDF, Epub and Kindle

Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome. Classically, the sample size n is much larger than p, the number of variables. The properties of statistical models have been mostly discussed under the assumption of fixed p and infinite n. The advance of biological sciences and technologies has revolutionized the process of investigations of cancer. The biomedical data collection has become more automatic and more extensive. We are in the era of p as a large fraction of n, and even much larger than n. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has been mostly a laborious process, carried out one protein at a time. The advent of high throughput proteome-wide technologies such as liquid chromatography-tandem mass spectroscopy make it possible to generate proteomic signatures that facilitate rapid development of new strategies for proteomics-based detection of disease. This poses new challenges and calls for scalable solutions to the analysis of such high dimensional data. In this volume, we will present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data.

Analysis of Multivariate and High Dimensional Data

Analysis of Multivariate and High Dimensional Data
Author: Inge Koch
Publsiher: Cambridge University Press
Total Pages: 531
Release: 2014
Genre: Business & Economics
ISBN: 9780521887939

Download Analysis of Multivariate and High Dimensional Data Book in PDF, Epub and Kindle

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

High Dimensional Statistics

High Dimensional Statistics
Author: Martin J. Wainwright
Publsiher: Cambridge University Press
Total Pages: 571
Release: 2019-02-21
Genre: Business & Economics
ISBN: 9781108498029

Download High Dimensional Statistics Book 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
Author: John Wright,Yi Ma
Publsiher: Cambridge University Press
Total Pages: 717
Release: 2022-01-13
Genre: Computers
ISBN: 9781108489737

Download High Dimensional Data Analysis with Low Dimensional Models Book in PDF, Epub and Kindle

Connects fundamental mathematical theory with real-world problems, through efficient and scalable optimization algorithms.

High dimensional Data Analysis

High dimensional Data Analysis
Author: Tianwen Tony Cai,Xiaotong Shen
Publsiher: World Scientific Publishing Company Incorporated
Total Pages: 307
Release: 2011
Genre: Mathematics
ISBN: 981432485X

Download High dimensional Data Analysis Book in PDF, Epub and Kindle

Over the last few years, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics and signal processing. In particular, substantial advances have been made in the areas of feature selection, covariance estimation, classification and regression. 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 research. The book will appeal to graduate students and new researchers interested in the plethora of opportunities available in high-dimensional data analysis.

Statistical Inference from High Dimensional Data

Statistical Inference from High Dimensional Data
Author: Carlos Fernandez-Lozano
Publsiher: MDPI
Total Pages: 314
Release: 2021-04-28
Genre: Science
ISBN: 9783036509440

Download Statistical Inference from High Dimensional Data Book in PDF, Epub and Kindle

• Real-world problems can be high-dimensional, complex, and noisy • More data does not imply more information • Different approaches deal with the so-called curse of dimensionality to reduce irrelevant information • A process with multidimensional information is not necessarily easy to interpret nor process • In some real-world applications, the number of elements of a class is clearly lower than the other. The models tend to assume that the importance of the analysis belongs to the majority class and this is not usually the truth • The analysis of complex diseases such as cancer are focused on more-than-one dimensional omic data • The increasing amount of data thanks to the reduction of cost of the high-throughput experiments opens up a new era for integrative data-driven approaches • Entropy-based approaches are of interest to reduce the dimensionality of high-dimensional data