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Robust nonlinear regression : with applications using R

Author: Hossein Riazoshams; Habshah Midi; Gebrenegus Ghilagaber
Publisher: Hoboken, NJ : John Wiley & Sons, Inc., [2019]. ©2019
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
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  Read more...
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Additional Physical Format: Online version:
Riazoshams, Hossein, 1971-
Robust nonlinear regression with application using R.
Hoboken, NJ : John Wiley & Sons, 2018
(DLC) 2018005931
Document Type: Book
All Authors / Contributors: Hossein Riazoshams; Habshah Midi; Gebrenegus Ghilagaber
ISBN: 9781118738061 1118738063
OCLC Number: 1022976989
Description: xv, 242 pages ; 24 cm
Contents: Robust statistics --
Nonlinear models --
Robust estimators in nonlinear regression --
Heteroscedastic variance --
Authocorrelated errors --
Outlier detection in nonlinear regression --
Optimization --
Nlr package --
Robust nonlinear regression in R.
Responsibility: Hossein Riazoshams, Habshah Midi, Gebrenegus Ghilagaber.

Abstract:

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. --

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