Robust Nonlinear Regression: with Applications using R
The first book to discuss robust aspects of nonlinear regressionwith 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
Hossein Riazoshams, PhD, is full-time Faculty member at the Department of Mathematics and Statistics, Lamerd Islamic Azad University of Iran.
Habshah Midi, PhD, is Professor at the Department of Mathematics, Faculty of Science and Institute for Mathematical Research, Universiti Putra Malaysia.
Gebrengus Ghilagaber, PhD, is Professor and Head at the Department of Statistics, Stockholm University.