Robust nonlinear regression: with application using R
By: Riazoshams, Hossein
Contributor(s): Midi, Habshah [Co author] | Ghilagaber, Gebrenegus [Co author]
Material type: 
TextPublisher: New Jersey John Wiley & Sons 2019Description: xv, 242p. With indexISBN: 9781118738061Subject(s): Nonlinear theories | R (computer program language) | Regression analysisDDC classification: 519.536 Summary: 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.
https://www.wiley.com/en-aw/Robust+Nonlinear+Regression%3A+with+Applications+using+R-p-9781118738061
| Item type | Current location | Item location | Collection | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|---|
| Books | Vikram Sarabhai Library General Stacks | Slot 1428 (0 Floor, East Wing) | Non-fiction | 519.536 R4R6 (Browse shelf) | Available | 198957 |
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| 519.536 L4S8 Sufficient dimension reduction: methods and applications with R | 519.536 M2S8 Statistical regression and classification: from linear models to machine learning | 519.536 M6I6 Introduction to linear regression analysis | 519.536 R4R6 Robust nonlinear regression: with application using R | 519.536 R8S3 Semiparametric Regression | 519.536 T2M2 The manga guide to regression analysis | 519.536 Y6H2 Handbook of regression methods |
TABLE OF CONTENTS
Part One Theories
1 Robust Statistics and its Application in Linear Regression 3
2 NonlinearModels: Concepts and Parameter Estimation 31
3 Robust Estimators in Nonlinear Regression 41
4 Heteroscedastic Variance 67
5 Autocorrelated Errors 89
6 Outlier Detection in Nonlinear Regression 107
Part Two Computations
7 Optimization 145
8 nlr Package 153
9 Robust Nonlinear Regression in R 207
A nlr Database 215
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.
https://www.wiley.com/en-aw/Robust+Nonlinear+Regression%3A+with+Applications+using+R-p-9781118738061
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