Nonlinear Statistical Models

Nonlinear Statistical Models
Author: A. Ronald Gallant
Publisher: John Wiley & Sons
Total Pages: 632
Release: 1987-02-04
Genre: Mathematics
ISBN:

Univariate nonlinear regression; Univariate nonlinear regression: special situations; A unified asymptotic theory of nonlinear models with regression structure; Univariate nonlinear regression: asymptotic theory; Multivariate nonlinear regression; Nonlinear simultaneus equations models; A unified asymptotic theory for dynamic nonlinear models.

Statistical Tools for Nonlinear Regression

Statistical Tools for Nonlinear Regression
Author: Sylvie Huet
Publisher: Springer Science & Business Media
Total Pages: 161
Release: 2013-04-17
Genre: Mathematics
ISBN: 147572523X

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.

Nonlinear Regression

Nonlinear Regression
Author: George A. F. Seber
Publisher: John Wiley & Sons
Total Pages: 800
Release: 2005-02-25
Genre: Mathematics
ISBN: 0471725307

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

Fitting Models to Biological Data Using Linear and Nonlinear Regression

Fitting Models to Biological Data Using Linear and Nonlinear Regression
Author: Harvey Motulsky
Publisher: Oxford University Press
Total Pages: 352
Release: 2004-05-27
Genre: Mathematics
ISBN: 9780198038344

Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists.

Applied Statistics in Agricultural, Biological, and Environmental Sciences

Applied Statistics in Agricultural, Biological, and Environmental Sciences
Author: Barry Glaz
Publisher: John Wiley & Sons
Total Pages: 672
Release: 2020-01-22
Genre: Technology & Engineering
ISBN: 0891183590

Better experimental design and statistical analysis make for more robust science. A thorough understanding of modern statistical methods can mean the difference between discovering and missing crucial results and conclusions in your research, and can shape the course of your entire research career. With Applied Statistics, Barry Glaz and Kathleen M. Yeater have worked with a team of expert authors to create a comprehensive text for graduate students and practicing scientists in the agricultural, biological, and environmental sciences. The contributors cover fundamental concepts and methodologies of experimental design and analysis, and also delve into advanced statistical topics, all explored by analyzing real agronomic data with practical and creative approaches using available software tools. IN PRESS! This book is being published according to the “Just Published” model, with more chapters to be published online as they are completed.

Nonlinear Regression with R

Nonlinear Regression with R
Author: Christian Ritz
Publisher: Springer Science & Business Media
Total Pages: 151
Release: 2008-12-11
Genre: Mathematics
ISBN: 0387096167

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

Nonlinear Models for Repeated Measurement Data

Nonlinear Models for Repeated Measurement Data
Author: Marie Davidian
Publisher: Routledge
Total Pages: 360
Release: 2017-11-01
Genre: Mathematics
ISBN: 1351428152

Nonlinear measurement data arise in a wide variety of biological and biomedical applications, such as longitudinal clinical trials, studies of drug kinetics and growth, and the analysis of assay and laboratory data. Nonlinear Models for Repeated Measurement Data provides the first unified development of methods and models for data of this type, with a detailed treatment of inference for the nonlinear mixed effects and its extensions. A particular strength of the book is the inclusion of several detailed case studies from the areas of population pharmacokinetics and pharmacodynamics, immunoassay and bioassay development and the analysis of growth curves.

Nonlinear Models in Medical Statistics

Nonlinear Models in Medical Statistics
Author: James K. Lindsey
Publisher:
Total Pages: 298
Release: 2001
Genre: Mathematics
ISBN: 9780198508120

This text provides an introduction to the use of nonlinear models in medical statistics. It is a practical text rather than a theoretical one and assumes a basic knowledge of statistical modelling and of generalized linear models. It begins with a general introduction to nonlinear models, comparing them to generalized linear models, descriptions of data handling and formula definition and a summary of the principal types of nonlinear regression formulae. There is an emphasis on techniques for non-normal data. Following chapters provide detailed examples of applications in various areas of medicine, epidemiology, clinical trials, quality of life, pharmokinetics, pharmacodynamics, assays and formulations, and moleuclar genetics.

Applied Linear Statistical Models

Applied Linear Statistical Models
Author: Michael H. Kutner
Publisher: McGraw-Hill/Irwin
Total Pages: 1396
Release: 2005
Genre: Mathematics
ISBN: 9780072386882

Linear regression with one predictor variable; Inferences in regression and correlation analysis; Diagnosticis and remedial measures; Simultaneous inferences and other topics in regression analysis; Matrix approach to simple linear regression analysis; Multiple linear regression; Nonlinear regression; Design and analysis of single-factor studies; Multi-factor studies; Specialized study designs.

Bayesian Methods for Nonlinear Classification and Regression

Bayesian Methods for Nonlinear Classification and Regression
Author: David G. T. Denison
Publisher: John Wiley & Sons
Total Pages: 302
Release: 2002-05-06
Genre: Mathematics
ISBN: 9780471490364

Bei der Regressionsanalyse von Datenmaterial erhält man leider selten lineare oder andere einfache Zusammenhänge (parametrische Modelle). Dieses Buch hilft Ihnen, auch komplexere, nichtparametrische Modelle zu verstehen und zu beherrschen. Stärken und Schwächen jedes einzelnen Modells werden durch die Anwendung auf Standarddatensätze demonstriert. Verbreitete nichtparametrische Modelle werden mit Hilfe von Bayes-Verfahren in einen kohärenten wahrscheinlichkeitstheoretischen Zusammenhang gebracht.