Nonlinear Regression

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Nonlinear Regression

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Author : George A. F. Seber
language : en
Publisher: John Wiley & Sons
Release Date : 2005-02-25

Nonlinear Regression written by George A. F. Seber 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 2005-02-25 with Mathematics categories.

WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. From the Reviews of Nonlinear Regression "A very good book and an important one in that it is likely to become a standard reference for all interested in nonlinear regression; and I would imagine that any statistician concerned with nonlinear regression would want a copy on his shelves." –The Statistician "Nonlinear Regression also includes a reference list of over 700 entries. The compilation of this material and cross-referencing of it is one of the most valuable aspects of the book. Nonlinear Regression can provide the researcher unfamiliar with a particular specialty area of nonlinear regression an introduction to that area of nonlinear regression and access to the appropriate references . . . Nonlinear Regression provides by far the broadest discussion of nonlinear regression models currently available and will be a valuable addition to the library of anyone interested in understanding and using such models including the statistical researcher." –Mathematical Reviews

Nonlinear Regression With R

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Author : Christian Ritz
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-12-11

Nonlinear Regression With R written by Christian Ritz 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 2008-12-11 with Mathematics categories.

R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. Currently, R offers a wide range of functionality for nonlinear regression analysis, but the relevant functions, packages and documentation are scattered across the R environment. This book provides a coherent and unified treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology. R. Subsequent chapters explain the salient features of the main fitting function nls (), the use of model diagnostics, how to deal with various model departures, and carry out hypothesis testing. In the final chapter grouped-data structures, including an example of a nonlinear mixed-effects regression model, are considered.

Statistical Tools For Nonlinear Regression

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Author : Sylvie Huet
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-18

Statistical Tools For Nonlinear Regression written by Sylvie Huet 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 2006-04-18 with Mathematics categories.

Statistical Tools for Nonlinear Regression presents methods for analyzing data. It has been expanded to include binomial, multinomial and Poisson non-linear models. The examples are analyzed with the free software nls2 updated to deal with the new models included in the second edition. The nls2 package is implemented in S-PLUS and R. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap.

Handbook Of Nonlinear Regression Models

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Author : David A. Ratkowsky
language : en
Publisher:
Release Date : 1990

Handbook Of Nonlinear Regression Models written by David A. Ratkowsky and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Mathematics categories.

The background; An introduction to regression modeling; Nonlinear regression modeling; An illustrative example of regression modeling; The models; Models with one X variable, convex/concave curves; Models with one X variable, sigmoidally shaped curves; Models with one X variable, curves with maxima and minima; Models with more than one explanatory viariable; Other models and excluded models; Obtaining good initial parameter estimates; Summary; References; Table of symbols; Appendix; Author index; Subject index.

Nonlinear Regression Analysis And Its Applications

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Author : Douglas M. Bates
language : en
Publisher: Wiley-Interscience
Release Date : 2007-04-23

Nonlinear Regression Analysis And Its Applications written by Douglas M. Bates and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-04-23 with Mathematics categories.

Provides a presentation of the theoretical, practical, and computational aspects of nonlinear regression. There is background material on linear regression, including a geometrical development for linear and nonlinear least squares.

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.

Nonlinear Regression Modeling

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Author : David A. Ratkowsky
language : en
Publisher:
Release Date : 1983

Nonlinear Regression Modeling written by David A. Ratkowsky and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with Mathematics categories.

Introduction to regression models; Assessing nonlinearity in nonlinear regression models; Yield-density models; Sigmoidal growth models; Asymptotic regression model; Some miscellaneous models; Comparing parameter estimates from more than one data set; Obtaining good initial parameter estimates; Summary: towatd a unified approach to nonlinear regression modeling.

Nonlinear Regression With R

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Author : Christian Ritz
language : en
Publisher: Springer
Release Date : 2008-11-01

Nonlinear Regression With R written by Christian Ritz and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-11-01 with Mathematics categories.

- Coherent and unified treatment of nonlinear regression with R. - Example-based approach. - Wide area of application.

Model Discrimination For Nonlinear Regression Models

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Author : Dale S. Borowiak
language : en
Publisher: CRC Press
Release Date : 2020-11-25

Model Discrimination For Nonlinear Regression Models written by Dale S. Borowiak and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Mathematics categories.

Borowiak (math, U. of Akron) discusses model discrimination based upon incorrect selection probability, presents diagnostic statistics and formal hypothesis test procedures to assess a model's fit and stability, explains the use of computer computations such as the jackknife and bootstrap, and demonstrates model discrimination techniques through examples taken from real experimental settings. Offering practical guidance that experimenters and students can apply to their own research problems, this definitive reference text is essential reading for statisticians, mathematicians, data analysts, forecasters, applied engineers and advanced undergraduate and graduate students in the above disciplines.

Asymptotic Theory Of Nonlinear Regression

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Author : A.A. Ivanov
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-17

Asymptotic Theory Of Nonlinear Regression written by A.A. Ivanov 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 2013-04-17 with Mathematics categories.

Let us assume that an observation Xi is a random variable (r.v.) with values in 1 1 (1R1 , 8 ) and distribution Pi (1R1 is the real line, and 8 is the cr-algebra of its Borel subsets). Let us also assume that the unknown distribution Pi belongs to a 1 certain parametric family {Pi() , () E e}. We call the triple £i = {1R1 , 8 , Pi(), () E e} a statistical experiment generated by the observation Xi. n We shall say that a statistical experiment £n = {lRn, 8 , P; ,() E e} is the product of the statistical experiments £i, i = 1, ... ,n if PO' = P () X ... X P () (IRn 1 n n is the n-dimensional Euclidean space, and 8 is the cr-algebra of its Borel subsets). In this manner the experiment £n is generated by n independent observations X = (X1, ... ,Xn). In this book we study the statistical experiments £n generated by observations of the form j = 1, ... ,n. (0.1) Xj = g(j, (}) + cj, c c In (0.1) g(j, (}) is a non-random function defined on e , where e is the closure in IRq of the open set e ~ IRq, and C j are independent r. v .-s with common distribution function (dJ.) P not depending on ().