Robust Nonlinear Regression: with Applications using R - Hardcover

Über die Autorin bzw. den Autor

Hossein Riazoshams, PhD, is a full-time Faculty member at the Department of Mathematics and Statistics, Lamerd Islamic Azad University of Iran; Stockholm University, Sweden; and University of Putra, Malaysia.

Habshah Midi, PhD, is Professor at the Department of Mathematics, Faculty of Science and Institute for Mathematical Research, University of Putra, Malaysia.

Gebrenegus Ghilagaber, PhD, is Professor and Head at the Department of Statistics, Stockholm University, Sweden.

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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 S-PLUS 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.

This book offers comprehensive coverage of the subject in nine chapters: Robust Statistics and its Application in Linear Regression; Nonlinear Models: Concepts and Parameter Estimation; Robust Estimators in Nonlinear Regression; Heteroscedastic Variance; Autocorrelated Errors; Outlier Detection in Nonlinear Regression; Optimization; nlr Package; and Robust Nonlinear Regression in R.

This book:

• Provides the first comprehensive coverage of this field which includes a variety of both theoretical and applied topics surrounding robust nonlinear regression
• Addresses some commonly mishandled aspects of modeling
• Details R packages for both classical and robust nonlinear regression which 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.

Aus dem Klappentext

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 S-PLUS 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.

This book offers comprehensive coverage of the subject in nine chapters: Robust Statistics and its Application in Linear Regression; Nonlinear Models: Concepts and Parameter Estimation; Robust Estimators in Nonlinear Regression; Heteroscedastic Variance; Autocorrelated Errors; Outlier Detection in Nonlinear Regression; Optimization; nlr Package; and Robust Nonlinear Regression in R.

This book:

• Provides the first comprehensive coverage of this field which includes a variety of both theoretical and applied topics surrounding robust nonlinear regression
• Addresses some commonly mishandled aspects of modeling
• Details R packages for both classical and robust nonlinear regression which 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.