Robust Nonlinear Regression
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Robust Nonlinear Regression
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Author : Hossein Riazoshams
language : en
Publisher: John Wiley & Sons
Release Date : 2018-08-20
Robust Nonlinear Regression written by Hossein Riazoshams and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-20 with Mathematics categories.
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.
Robust Non Linear Regression Using The Dogleg Algorithm
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Author : Roy E. Welsch
language : en
Publisher:
Release Date : 1975
Robust Non Linear Regression Using The Dogleg Algorithm written by Roy E. Welsch and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1975 with categories.
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.
Robust Regression
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Author : Kenneth D. Lawrence
language : en
Publisher: Routledge
Release Date : 2019-05-20
Robust Regression written by Kenneth D. Lawrence and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-20 with Mathematics categories.
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.
Robust Estimation In Nonlinear Regression And Limited Dependent Variable Models
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Author : Pavel Čížek
language : en
Publisher:
Release Date : 2001
Robust Estimation In Nonlinear Regression And Limited Dependent Variable Models written by Pavel Čížek and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with categories.
Nonlinear Regression Functional Relations And Robust Methods
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Author : Helga Bunke
language : en
Publisher:
Release Date : 1989
Nonlinear Regression Functional Relations And Robust Methods written by Helga Bunke and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Mathematics categories.
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.
Robust Diagnostic Regression Analysis
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Author : Anthony Atkinson
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Robust Diagnostic Regression Analysis written by Anthony Atkinson and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
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.
Robust Estimation In Nonlinear Regression Models
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Author : Pavel Čížek
language : en
Publisher:
Release Date : 2001
Robust Estimation In Nonlinear Regression Models written by Pavel Čížek and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with categories.
Robust Nonlinear Regression
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Author : Hossein Riazoshams
language : en
Publisher: John Wiley & Sons
Release Date : 2018-06-11
Robust Nonlinear Regression written by Hossein Riazoshams and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-11 with Mathematics categories.
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.
Robust Methods And Asymptotic Theory In Nonlinear Econometrics
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Author : H. J. Bierens
language : en
Publisher: Springer
Release Date : 2012-03-02
Robust Methods And Asymptotic Theory In Nonlinear Econometrics written by H. J. Bierens and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-02 with Mathematics categories.
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.
Statistical Methods Of Model Building Nonlinear Regression Functional Relations And Robust Methods
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Author : Helga Bunke
language : en
Publisher:
Release Date : 1986
Statistical Methods Of Model Building Nonlinear Regression Functional Relations And Robust Methods written by Helga Bunke and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986 with Mathematical statistics categories.