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| Author | : Georgy L. Shevlyakov |
| Publisher | : John Wiley & Sons |
| Total Pages | : 352 |
| Release | : 2016-08-02 |
| Genre | : Mathematics |
| ISBN | : 1119264537 |
Download Robust Correlation Book in PDF, ePub and Kindle
This bookpresents material on both the analysis of the classical concepts of correlation and on the development of their robust versions, as well as discussing the related concepts of correlation matrices, partial correlation, canonical correlation, rank correlations, with the corresponding robust and non-robust estimation procedures. Every chapter contains a set of examples with simulated and real-life data. Key features: Makes modern and robust correlation methods readily available and understandable to practitioners, specialists, and consultants working in various fields. Focuses on implementation of methodology and application of robust correlation with R. Introduces the main approaches in robust statistics, such as Huber’s minimax approach and Hampel’s approach based on influence functions. Explores various robust estimates of the correlation coefficient including the minimax variance and bias estimates as well as the most B- and V-robust estimates. Contains applications of robust correlation methods to exploratory data analysis, multivariate statistics, statistics of time series, and to real-life data. Includes an accompanying website featuring computer code and datasets Features exercises and examples throughout the text using both small and large data sets. Theoretical and applied statisticians, specialists in multivariate statistics, robust statistics, robust time series analysis, data analysis and signal processing will benefit from this book. Practitioners who use correlation based methods in their work as well as postgraduate students in statistics will also find this book useful.
| Author | : Georgy L. Shevlyakov |
| Publisher | : John Wiley & Sons |
| Total Pages | : 353 |
| Release | : 2016-09-19 |
| Genre | : Mathematics |
| ISBN | : 1118493451 |
Download Robust Correlation Book in PDF, ePub and Kindle
This bookpresents material on both the analysis of the classical concepts of correlation and on the development of their robust versions, as well as discussing the related concepts of correlation matrices, partial correlation, canonical correlation, rank correlations, with the corresponding robust and non-robust estimation procedures. Every chapter contains a set of examples with simulated and real-life data. Key features: Makes modern and robust correlation methods readily available and understandable to practitioners, specialists, and consultants working in various fields. Focuses on implementation of methodology and application of robust correlation with R. Introduces the main approaches in robust statistics, such as Huber’s minimax approach and Hampel’s approach based on influence functions. Explores various robust estimates of the correlation coefficient including the minimax variance and bias estimates as well as the most B- and V-robust estimates. Contains applications of robust correlation methods to exploratory data analysis, multivariate statistics, statistics of time series, and to real-life data. Includes an accompanying website featuring computer code and datasets Features exercises and examples throughout the text using both small and large data sets. Theoretical and applied statisticians, specialists in multivariate statistics, robust statistics, robust time series analysis, data analysis and signal processing will benefit from this book. Practitioners who use correlation based methods in their work as well as postgraduate students in statistics will also find this book useful.
| Author | : Chris Tofallis |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2013 |
| Genre | : |
| ISBN | : |
Download Robust Correlation Measures Book in PDF, ePub and Kindle
It is well established that the standard measure of correlation (Pearson's product-moment) is very sensitive to outliers. It can give extremely misleading results when a few or even a single outlier is present. A number of robust correlation measures have been proposed. We do not consider estimators which require trimming (discarding) of some arbitrary fraction of the data, nor those which require iterative computation. Our overall aim is to find a practical and simple robust measure of correlation which can be recommended to practitioners alongside the classic Pearson and Spearman measures. The well known data sets of Anscombe are used to provide an initial assessment of these estimators. These four data sets were designed to have identical Pearson correlation coefficients as well as identical regression lines and other regression statistics. Nevertheless, visual inspection of their scatter-graphs indicates very different patterns. For data set C, there is a perfect linear relationship for all but one of the data points; whereas for data set D, apart from one outlier, all points have the same x-value and so there is essentially no co-variation or interdependence between the variables. We prefer a robust correlation measure to have a near-zero value for set D, and a high value for set C, with the other two data sets giving an intermediate value.
| Author | : Savas Papadopoulos |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2022 |
| Genre | : |
| ISBN | : |
Download A Practical, Powerful, Robust and Interpretable Family of Correlation Coefficients Book in PDF, ePub and Kindle
If we conducted a competition for which statistical quantity would be the most valuable in exploratory data analysis, the winner would most likely be the correlation coefficient with a significant difference from its first competitor. In addition, most data applications contain non-normal data with outliers without being able to be converted to normal data. Therefore, we search for robust correlation coefficients to nonnormality and outliers that could be applied to all applications and detect influenced or hidden correlations not recognized by the most popular correlation coefficients. We introduce a correlation-coefficient family with the Pearson and Spearman coefficients as specific cases. Other family members provide desirable lower p-values than those derived by the standard coefficients in the earlier problems. The proposed family of coefficients, their cut-off points, and p-values, computed by permutation tests, could be applied by all scientists analyzing data. We share simulations, code, and real data by email or the internet.
| Author | : Rand R. Wilcox |
| Publisher | : Academic Press |
| Total Pages | : 713 |
| Release | : 2011-12-14 |
| Genre | : Mathematics |
| ISBN | : 0123870151 |
Download Introduction to Robust Estimation and Hypothesis Testing Book in PDF, ePub and Kindle
This revised book provides a thorough explanation of the foundation of robust methods, incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and regression. It guides advanced students and other professionals through the basic strategies used for developing practical solutions to problems, and provides a brief background on the foundations of modern methods, placing the new methods in historical context. Author Rand Wilcox includes chapter exercises and many real-world examples that illustrate how various methods perform in different situations. Introduction to Robust Estimation and Hypothesis Testing, Second Edition, focuses on the practical applications of modern, robust methods which can greatly enhance our chances of detecting true differences among groups and true associations among variables. Covers latest developments in robust regression Covers latest improvements in ANOVA Includes newest rank-based methods Describes and illustrated easy to use software
| Author | : Sander Gerber |
| Publisher | : |
| Total Pages | : 7 |
| Release | : 2019 |
| Genre | : |
| ISBN | : |
Download The Gerber Statistic Book in PDF, ePub and Kindle
We introduce the Gerber statistic, a robust measure of correlation. The statistic extends Kendall's Tau by counting the proportion of simultaneous co-movements in series when their amplitudes exceed data-dependent thresholds. This is unlike the standard Pearson correlation that is sensitive to outliers or the Spearman correlation that relies on ranking observations. Since the statistic is neither affected by extremely large or extremely small movements, it is especially suited to financial time series since these can exhibit extreme movements as well as a great amount of noise. Therefore, the statistic can advantageously be converted into a robust estimate of a covariance matrix that is suitable for portfolio optimization.
| Author | : Georgy L. Shevlyakov |
| Publisher | : Walter de Gruyter |
| Total Pages | : 325 |
| Release | : 2011-12-07 |
| Genre | : Mathematics |
| ISBN | : 3110936003 |
Download Robustness in Data Analysis Book in PDF, ePub and Kindle
The series is devoted to the publication of high-level monographs and surveys which cover the whole spectrum of probability and statistics. The books of the series are addressed to both experts and advanced students.
| Author | : Frank J. Fabozzi |
| Publisher | : John Wiley & Sons |
| Total Pages | : 517 |
| Release | : 2007-06-04 |
| Genre | : Business & Economics |
| ISBN | : 047192122X |
Download Robust Portfolio Optimization and Management Book in PDF, ePub and Kindle
Praise for Robust Portfolio Optimization and Management "In the half century since Harry Markowitz introduced his elegant theory for selecting portfolios, investors and scholars have extended and refined its application to a wide range of real-world problems, culminating in the contents of this masterful book. Fabozzi, Kolm, Pachamanova, and Focardi deserve high praise for producing a technically rigorous yet remarkably accessible guide to the latest advances in portfolio construction." --Mark Kritzman, President and CEO, Windham Capital Management, LLC "The topic of robust optimization (RO) has become 'hot' over the past several years, especially in real-world financial applications. This interest has been sparked, in part, by practitioners who implemented classical portfolio models for asset allocation without considering estimation and model robustness a part of their overall allocation methodology, and experienced poor performance. Anyone interested in these developments ought to own a copy of this book. The authors cover the recent developments of the RO area in an intuitive, easy-to-read manner, provide numerous examples, and discuss practical considerations. I highly recommend this book to finance professionals and students alike." --John M. Mulvey, Professor of Operations Research and Financial Engineering, Princeton University
| Author | : Hossein Riazoshams |
| Publisher | : John Wiley & Sons |
| Total Pages | : 264 |
| Release | : 2018-06-11 |
| Genre | : Mathematics |
| ISBN | : 1119010454 |
Download Robust Nonlinear Regression Book in PDF, ePub and Kindle
The first book to discuss robust aspects of nonlinear regression—with applications using R software Robust Nonlinear Regression: with Applications using R covers a variety of theories and applications of nonlinear robust regression. It discusses both parts of the classic and robust aspects of nonlinear regression and focuses on outlier effects. It develops new methods in robust nonlinear regression and implements a set of objects and functions in S-language under SPLUS and R software. The software covers a wide range of robust nonlinear fitting and inferences, and is designed to provide facilities for computer users to define their own nonlinear models as an object, and fit models using classic and robust methods as well as detect outliers. The implemented objects and functions can be applied by practitioners as well as researchers. The book offers comprehensive coverage of the subject in 9 chapters: Theories of Nonlinear Regression and Inference; Introduction to R; Optimization; Theories of Robust Nonlinear Methods; Robust and Classical Nonlinear Regression with Autocorrelated and Heteroscedastic errors; Outlier Detection; R Packages in Nonlinear Regression; A New R Package in Robust Nonlinear Regression; and Object Sets. The first comprehensive coverage of this field covers a variety of both theoretical and applied topics surrounding robust nonlinear regression Addresses some commonly mishandled aspects of modeling R packages for both classical and robust nonlinear regression are presented in detail in the book and on an accompanying website Robust Nonlinear Regression: with Applications using R is an ideal text for statisticians, biostatisticians, and statistical consultants, as well as advanced level students of statistics.
| Author | : Rand R. Wilcox |
| Publisher | : Academic Press |
| Total Pages | : 713 |
| Release | : 2012-01-12 |
| Genre | : Mathematics |
| ISBN | : 0123869838 |
Download Introduction to Robust Estimation and Hypothesis Testing Book in PDF, ePub and Kindle
"This book focuses on the practical aspects of modern and robust statistical methods. The increased accuracy and power of modern methods, versus conventional approaches to the analysis of variance (ANOVA) and regression, is remarkable. Through a combination of theoretical developments, improved and more flexible statistical methods, and the power of the computer, it is now possible to address problems with standard methods that seemed insurmountable only a few years ago"--
