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Robust Nonlinear Regression
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Book Synopsis Robust Nonlinear Regression by : Hossein Riazoshams
Download or read book Robust Nonlinear Regression written by Hossein Riazoshams and published by John Wiley & Sons. This book was released on 2018-08-20 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Robust Nonlinear Regression by : Hossein Riazoshams
Download or read book Robust Nonlinear Regression written by Hossein Riazoshams and published by John Wiley & Sons. This book was released on 2018-06-11 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Robust Regression by : Kenneth D. Lawrence
Download or read book Robust Regression written by Kenneth D. Lawrence and published by Routledge. This book was released on 2019-05-20 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust Regression: Analysis and Applications characterizes robust estimators in terms of how much they weight each observation discusses generalized properties of Lp-estimators. Includes an algorithm for identifying outliers using least absolute value criterion in regression modeling reviews redescending M-estimators studies Li linear regression proposes the best linear unbiased estimators for fixed parameters and random errors in the mixed linear model summarizes known properties of Li estimators for time series analysis examines ordinary least squares, latent root regression, and a robust regression weighting scheme and evaluates results from five different robust ridge regression estimators.
Book Synopsis Robust Estimation in Nonlinear Regression and Limited Dependent Variable Models by : Pavel Čížek
Download or read book Robust Estimation in Nonlinear Regression and Limited Dependent Variable Models written by Pavel Čížek and published by . This book was released on 2001 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Methods and Asymptotic Theory in Nonlinear Econometrics by : H. J. Bierens
Download or read book Robust Methods and Asymptotic Theory in Nonlinear Econometrics written by H. J. Bierens and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Lecture Note deals with asymptotic properties, i.e. weak and strong consistency and asymptotic normality, of parameter estimators of nonlinear regression models and nonlinear structural equations under various assumptions on the distribution of the data. The estimation methods involved are nonlinear least squares estimation (NLLSE), nonlinear robust M-estimation (NLRME) and non linear weighted robust M-estimation (NLWRME) for the regression case and nonlinear two-stage least squares estimation (NL2SLSE) and a new method called minimum information estimation (MIE) for the case of structural equations. The asymptotic properties of the NLLSE and the two robust M-estimation methods are derived from further elaborations of results of Jennrich. Special attention is payed to the comparison of the asymptotic efficiency of NLLSE and NLRME. It is shown that if the tails of the error distribution are fatter than those of the normal distribution NLRME is more efficient than NLLSE. The NLWRME method is appropriate if the distributions of both the errors and the regressors have fat tails. This study also improves and extends the NL2SLSE theory of Amemiya. The method involved is a variant of the instrumental variables method, requiring at least as many instrumental variables as parameters to be estimated. The new MIE method requires less instrumental variables. Asymptotic normality can be derived by employing only one instrumental variable and consistency can even be proved with out using any instrumental variables at all.
Book Synopsis Nonlinear Regression with R by : Christian Ritz
Download or read book Nonlinear Regression with R written by Christian Ritz and published by Springer Science & Business Media. This book was released on 2008-12-11 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: - Coherent and unified treatment of nonlinear regression with R. - Example-based approach. - Wide area of application.
Book Synopsis Robust Non-Linear Regression Using The Dogleg Algorithm by : Roy E. Welsch
Download or read book Robust Non-Linear Regression Using The Dogleg Algorithm written by Roy E. Welsch and published by . This book was released on 1975 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: What are the statistical and computational problems associated with robust nonlinear regression? This paper presents a number of possible approaches to these problems and develops a particular algorithm based on the work of Powell and Dennis.
Book Synopsis Modern Methods for Robust Regression by : Robert Andersen
Download or read book Modern Methods for Robust Regression written by Robert Andersen and published by SAGE. This book was released on 2008 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offering an in-depth treatment of robust and resistant regression, this volume takes an applied approach and offers readers empirical examples to illustrate key concepts.
Book Synopsis Robust Diagnostic Regression Analysis by : Anthony Atkinson
Download or read book Robust Diagnostic Regression Analysis written by Anthony Atkinson and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graphs are used to understand the relationship between a regression model and the data to which it is fitted. The authors develop new, highly informative graphs for the analysis of regression data and for the detection of model inadequacies. As well as illustrating new procedures, the authors develop the theory of the models used, particularly for generalized linear models. The book provides statisticians and scientists with a new set of tools for data analysis. Software to produce the plots is available on the authors website.
Book Synopsis Fitting Models to Biological Data Using Linear and Nonlinear Regression by : Harvey Motulsky
Download or read book Fitting Models to Biological Data Using Linear and Nonlinear Regression written by Harvey Motulsky and published by Oxford University Press. This book was released on 2004-05-27 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists.
Book Synopsis Robust Statistics by : Ricardo A. Maronna
Download or read book Robust Statistics written by Ricardo A. Maronna and published by John Wiley & Sons. This book was released on 2019-01-04 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new edition of this popular text on robust statistics, thoroughly updated to include new and improved methods and focus on implementation of methodology using the increasingly popular open-source software R. Classical statistics fail to cope well with outliers associated with deviations from standard distributions. Robust statistical methods take into account these deviations when estimating the parameters of parametric models, thus increasing the reliability of fitted models and associated inference. This new, second edition of Robust Statistics: Theory and Methods (with R) presents a broad coverage of the theory of robust statistics that is integrated with computing methods and applications. Updated to include important new research results of the last decade and focus on the use of the popular software package R, it features in-depth coverage of the key methodology, including regression, multivariate analysis, and time series modeling. The book is illustrated throughout by a range of examples and applications that are supported by a companion website featuring data sets and R code that allow the reader to reproduce the examples given in the book. Unlike other books on the market, Robust Statistics: Theory and Methods (with R) offers the most comprehensive, definitive, and up-to-date treatment of the subject. It features chapters on estimating location and scale; measuring robustness; linear regression with fixed and with random predictors; multivariate analysis; generalized linear models; time series; numerical algorithms; and asymptotic theory of M-estimates. Explains both the use and theoretical justification of robust methods Guides readers in selecting and using the most appropriate robust methods for their problems Features computational algorithms for the core methods Robust statistics research results of the last decade included in this 2nd edition include: fast deterministic robust regression, finite-sample robustness, robust regularized regression, robust location and scatter estimation with missing data, robust estimation with independent outliers in variables, and robust mixed linear models. Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is an ideal resource for researchers, practitioners, and graduate students in statistics, engineering, computer science, and physical and social sciences.
Book Synopsis Nonlinear Regression by : George A. F. Seber
Download or read book Nonlinear Regression written by George A. F. Seber and published by John Wiley & Sons. This book was released on 2005-02-25 with total page 768 pages. Available in PDF, EPUB and Kindle. Book excerpt: WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. From the Reviews of Nonlinear Regression "A very good book and an important one in that it is likely to become a standard reference for all interested in nonlinear regression; and I would imagine that any statistician concerned with nonlinear regression would want a copy on his shelves." –The Statistician "Nonlinear Regression also includes a reference list of over 700 entries. The compilation of this material and cross-referencing of it is one of the most valuable aspects of the book. Nonlinear Regression can provide the researcher unfamiliar with a particular specialty area of nonlinear regression an introduction to that area of nonlinear regression and access to the appropriate references . . . Nonlinear Regression provides by far the broadest discussion of nonlinear regression models currently available and will be a valuable addition to the library of anyone interested in understanding and using such models including the statistical researcher." –Mathematical Reviews
Book Synopsis Nonlinear Regression, Functional Relations and Robust Methods by : Helga Bunke
Download or read book Nonlinear Regression, Functional Relations and Robust Methods written by Helga Bunke and published by . This book was released on 1989 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, the second volume in a three part work, provides a comprehensive and unified account of nonlinear regression analysis, functional and structural relations, and of nonparametric and robust estimators. Research in these areas has been stimulated by the increase in computational capabilities and this volume will therefore be of great interest to researchers in statistics as well as applied statisticians working in industry. The material provided includes recent work from German and Russian sources, as well as from English-speaking sources, and the treatment throughout is mathematically rigorous but accessible. The text will benefit rsearchers in statistics and applied statisticians working in industry.
Book Synopsis Robust Regression and Outlier Detection by : Peter J. Rousseeuw
Download or read book Robust Regression and Outlier Detection written by Peter J. Rousseeuw and published by John Wiley & Sons. This book was released on 2005-02-25 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selectedbooks that have been made more accessible to consumers in an effortto increase global appeal and general circulation. With these newunabridged softcover volumes, Wiley hopes to extend the lives ofthese works by making them available to future generations ofstatisticians, mathematicians, and scientists. "The writing style is clear and informal, and much of thediscussion is oriented to application. In short, the book is akeeper." –Mathematical Geology "I would highly recommend the addition of this book to thelibraries of both students and professionals. It is a usefultextbook for the graduate student, because it emphasizes both thephilosophy and practice of robustness in regression settings, andit provides excellent examples of precise, logical proofs oftheorems. . . .Even for those who are familiar with robustness, thebook will be a good reference because it consolidates the researchin high-breakdown affine equivariant estimators and includes anextensive bibliography in robust regression, outlier diagnostics,and related methods. The aim of this book, the authors tell us, is‘to make robust regression available for everyday statisticalpractice.’ Rousseeuw and Leroy have included all of thenecessary ingredients to make this happen." –Journal of the American Statistical Association
Book Synopsis Statistical Methods of Model Building: Nonlinear regression, functional relations, and robust methods by : Helga Bunke
Download or read book Statistical Methods of Model Building: Nonlinear regression, functional relations, and robust methods written by Helga Bunke and published by . This book was released on 1986 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Directions in Robust Statistics and Diagnostics by : Werner Stahel
Download or read book Directions in Robust Statistics and Diagnostics written by Werner Stahel and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IMA Volume in Mathematics and its Applications DIRECTIONS IN ROBUST STATISTICS AND DIAGNOSTICS is based on the proceedings of the first four weeks of the six week IMA 1989 summer program "Robustness, Diagnostics, Computing and Graphics in Statistics". An important objective of the organizers was to draw a broad set of statisticians working in robustness or diagnostics into collaboration on the challenging problems in these areas, particularly on the interface between them. We thank the organizers of the robustness and diagnostics program Noel Cressie, Thomas P. Hettmansperger, Peter J. Huber, R. Douglas Martin, and especially Werner Stahel and Sanford Weisberg who edited the proceedings. A vner Friedman Willard Miller, Jr. PREFACE Central themes of all statistics are estimation, prediction, and making decisions under uncertainty. A standard approach to these goals is through parametric mod elling. Parametric models can give a problem sufficient structure to allow standard, well understood paradigms to be applied to make the required inferences. If, how ever, the parametric model is not completely correct, then the standard inferential methods may not give reasonable answers. In the last quarter century, particularly with the advent of readily available computing, more attention has been paid to the problem of inference when the parametric model used is not correctly specified.
Book Synopsis Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference by : Lazaros Iliadis
Download or read book Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference written by Lazaros Iliadis and published by Springer Nature. This book was released on 2020-05-27 with total page 630 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers the proceedings of the 21st Engineering Applications of Neural Networks Conference, which is supported by the International Neural Networks Society (INNS). Artificial Intelligence (AI) has been following a unique course, characterized by alternating growth spurts and “AI winters.” Today, AI is an essential component of the fourth industrial revolution and enjoying its heyday. Further, in specific areas, AI is catching up with or even outperforming human beings. This book offers a comprehensive guide to AI in a variety of areas, concentrating on new or hybrid AI algorithmic approaches with robust applications in diverse sectors. One of the advantages of this book is that it includes robust algorithmic approaches and applications in a broad spectrum of scientific fields, namely the use of convolutional neural networks (CNNs), deep learning and LSTM in robotics/machine vision/engineering/image processing/medical systems/the environment; machine learning and meta learning applied to neurobiological modeling/optimization; state-of-the-art hybrid systems; and the algorithmic foundations of artificial neural networks.
