Robust Statistics for Signal Processing - 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 Robust Statistics for Signal Processing write by Abdelhak M. Zoubir. This book was released on 2018-11-08. Robust Statistics for Signal Processing available in PDF, EPUB and Kindle. Understand the benefits of robust statistics for signal processing with this authoritative yet accessible text. The first ever book on the subject, it provides a comprehensive overview of the field, moving from fundamental theory through to important new results and recent advances. Topics covered include advanced robust methods for complex-valued data, robust covariance estimation, penalized regression models, dependent data, robust bootstrap, and tensors. Robustness issues are illustrated throughout using real-world examples and key algorithms are included in a MATLAB Robust Signal Processing Toolbox accompanying the book online, allowing the methods discussed to be easily applied and adapted to multiple practical situations. This unique resource provides a powerful tool for researchers and practitioners working in the field of signal processing.
Robust and Multivariate Statistical Methods
Robust and Multivariate Statistical Methods - 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 Robust and Multivariate Statistical Methods write by Mengxi Yi. This book was released on 2023-04-19. Robust and Multivariate Statistical Methods available in PDF, EPUB and Kindle. This book presents recent developments in multivariate and robust statistical methods. Featuring contributions by leading experts in the field it covers various topics, including multivariate and high-dimensional methods, time series, graphical models, robust estimation, supervised learning and normal extremes. It will appeal to statistics and data science researchers, PhD students and practitioners who are interested in modern multivariate and robust statistics. The book is dedicated to David E. Tyler on the occasion of his pending retirement and also includes a review contribution on the popular Tyler’s shape matrix.
Modern Nonparametric, Robust and Multivariate Methods
Modern Nonparametric, Robust and Multivariate Methods - 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 Modern Nonparametric, Robust and Multivariate Methods write by Klaus Nordhausen. This book was released on 2015-10-05. Modern Nonparametric, Robust and Multivariate Methods available in PDF, EPUB and Kindle. Written by leading experts in the field, this edited volume brings together the latest findings in the area of nonparametric, robust and multivariate statistical methods. The individual contributions cover a wide variety of topics ranging from univariate nonparametric methods to robust methods for complex data structures. Some examples from statistical signal processing are also given. The volume is dedicated to Hannu Oja on the occasion of his 65th birthday and is intended for researchers as well as PhD students with a good knowledge of statistics.
Topics in Robust Statistical Signal Processing
Topics in Robust Statistical Signal Processing - 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 Topics in Robust Statistical Signal Processing write by Kenneth Steven Vastola. This book was released on 1982. Topics in Robust Statistical Signal Processing available in PDF, EPUB and Kindle. This dissertation addresses several problems in robust signal processing. The term robust in this context implies insensitivity to small deviations from the assumed statistical description of the signal and/or noise. The first part of this thesis considers the problem of linear minimum-mean-square-error estimation of a stationary signal observed in additive stationary noise when knowledge of the signal spectrum and noise spectrum is inexact. In the second part of this dissertation, a previously developed cohesive theory of robust hypothesis testing in which uncertainty is modeled via 2-alternating Choquet capacity classes is considered in light of recent applications of this theory to problems in robust signal processing and communication theory.
Robustness in Data Analysis
Robustness in Data Analysis - 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 Robustness in Data Analysis write by Georgij Leonidovič Ševljakov. This book was released on 2002. Robustness in Data Analysis available in PDF, EPUB and Kindle. The field of mathematical statistics called robustness statistics deals with the stability of statistical inference under variations of accepted distribution models. Although robust statistics involves mathematically highly defined tools, robust methods exhibit a satisfactory behaviour in small samples, thus being quite useful in applications. This volume in the book series Modern Probability and Statistics addresses various topics in the field of robust statistics and data analysis, such as: a probability-free approach in data analysis; minimax variance estimators of location, scale, regression, autoregression and correlation; "L1-norm methods; adaptive, data reduction, bivariate boxplot, and multivariate outlier detection algorithms; applications in reliability, detection of signals, and analysis of the sudden cardiac death risk factors. The book contains new results related to robustness and data analysis technologies, including both theoretical aspects and practical needs of data processing, which have been relatively inaccessible as they were originally only published in Russian. This book will be of value and interest to researchers in mathematical statistics as well as to those using statistical methods.
