Statistical Tools for Nonlinear Regression: A Practical Guide with S-PLUS and R Examples (Springer Series in Statistics)

Sylvie Huet, Anne Bouvier, Marie-Anne Poursat, Emmanuel Jolivet

ISBN 10: 0387400818 / ISBN 13: 9780387400815

Published by Springer, 2003

About this Item

Hardcover, xiv + 232 pages, second ed., NOT ex-library. Printed in the USA. Mild internal creasing, else very good. Book is clean and bright with unmarked text and firm binding, free of inscriptions and stamps. Issued without a dust jacket. -- Contents: 1 Nonlinear Regression Model and Parameter Estimation [Examples; Parametric Nonlinear Regression Model; Estimation; Applications (Pasture Regrowth; Cortisol Assay; ELISA Test; Ovocytes; Isomerization); Conclusion and References; Using nls2] 2 Accuracy of Estimators, Confidence Intervals and Tests [Examples; Problem Formulation; Solutions; Applications] 3 Variance Estimation [Examples; Parametric Modeling of the Variance; Estimation (Maximum Likelihood; Quasi-Likelihood; Three-Step Estimation); Tests and Confidence Regions; Applications (Growth of Winter Wheat Tillers; Solubility of Peptides in Trichloacetic Acid Solutions] 4 Diagnostics of Model Misspecification [Problem Formulation; Diagnostics of Model Misspecifications with Graphics; Diagnostics of Model Misspecifications with Tests; Numerical Troubles During the Estimation Process: Peptides Example; Peptides Example: Concluded] 5 Calibration and Prediction [Examples; Problem Formulation; Confidence Intervals; Applications] 6 Binomial Nonlinear Models [Examples (Assay of an Insecticide with a Synergist: A Binomial Nonlinear Model; Vaso-Constriction in the Skin of the Digits: The Case of Binary Response Data; Mortality of Confused Flour Beetles: The Choice of a Link Function in a Binomial Linear Model; Mortality of Confused Flour Beetles 2: Survival Analysis Using a Binomial Nonlinear Model; Germination of Orobranche: Overdispersion); Parametric Binomial Nonlinear Model; Overdispersion, Underdispersion; Estimation; Tests and Confidence Regions; Applications] 7 Multinomial and Poisson Nonlinear Models [Multinomial Model (Pneumoconiosis among Coal Miners: An Example of Multicategory Response Data; A Cheese Tasting Experiment; Parametric Multinomial Model; Estimation in the Multinomial Model; Tests and Confidence Intervals; Pneumoconiosis among Coal Miners: The Multinomial Logit Model; Cheese Tasting Example: Model Based on Cumulative Probabilities; Using nls2); Poisson Model]; References; Index -- Statistical Tools for Nonlinear Regression, Second Edition, presents methods for analyzing data using parametric nonlinear regression models. The new edition has been expanded to include binomial, multinomial and Poisson non-linear models. Using examples from experiments in agronomy and biochemistry, it shows how to apply these methods. It concentrates on presenting the methods in an intuitive way rather than developing the theoretical backgrounds. 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. Its main advantages are to make the model building, estimation and validation tasks, easy to do. More precisely, Complex models can be easily described using a symbolic syntax. The regression function as well as the variance function can be defined explicitly as functions of independent variables and of unknown parameters or they can be defined as the solution to a system of differential equations. Moreover, constraints on the parameters can easily be added to the model. It is thus possible to test nested hypotheses and to compare several data sets. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap. Some graphical tools are proposed for visualizing the fitted curves, the residuals, the confidence regions, and the numerical estimation procedure.

Seller Inventory # 005884

Bibliographic Details

• Title: Statistical Tools for Nonlinear Regression: A Practical Guide with S-PLUS and R Examples (Springer Series in Statistics)
• Publisher: Springer
• Publication Date: 2003
• Language: English
• ISBN 10: 0387400818
• ISBN 13: 9780387400815
• Binding: Hardcover
• Edition: 2nd Edition
• Condition: Very Good

About this title

Synopsis

Statistical Tools for Nonlinear Regression, Second Edition, presents methods for analyzing data using parametric nonlinear regression models. The new edition has been expanded to include binomial, multinomial and Poisson non-linear models. Using examples from experiments in agronomy and biochemistry, it shows how to apply these methods. It concentrates on presenting the methods in an intuitive way rather than developing the theoretical backgrounds. 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. Its main advantages are to make the model building, estimation and validation tasks, easy to do. More precisely, Complex models can be easily described using a symbolic syntax. The regression function as well as the variance function can be defined explicitly as functions of independent variables and of unknown parameters or they can be defined as the solution to a system of differential equations. Moreover, constraints on the parameters can easily be added to the model. It is thus possible to test nested hypotheses and to compare several data sets. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap. Some graphical tools are proposed for visualizing the fitted curves, the residuals, the confidence regions, and the numerical estimation procedure.

From the Back Cover

Statistical Tools for Nonlinear Regression, (Second Edition), presents methods for analyzing data using parametric nonlinear regression models. The new edition has been expanded to include binomial, multinomial and Poisson non-linear models. Using examples from experiments in agronomy and biochemistry, it shows how to apply these methods. It concentrates on presenting the methods in an intuitive way rather than developing the theoretical backgrounds.

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. Its main advantages are to make the model building, estimation and validation tasks, easy to do. More precisely,

Complex models can be easily described using a symbolic syntax. The regression function as well as the variance function can be defined explicitly as functions of independent variables and of unknown parameters or they can be defined as the solution to a system of differential equations. Moreover, constraints on the parameters can easily be added to the model. It is thus possible to test nested hypotheses and to compare several data sets.

Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap. Some graphical tools are proposed for visualizing the fitted curves, the residuals, the confidence regions, and the numerical estimation procedure.

This book is aimed at scientists who are not familiar with statistical theory, but have a basic knowledge of statistical concepts. It includes methods based on classical nonlinear regression theory and more modern methods, such as bootstrap, which have proved effective in practice. The additional chapters of the second edition assume some practical experience in data analysis using generalized linear models. The book will be of interest both for practitioners as a guide and a reference book, and for students, as a tutorial book.

Sylvie Huet and Emmanuel Jolivet are senior researchers and Annie Bouvier is computing engineer at INRA, National Institute of Agronomical Research, France; Marie-Anne Poursat is associate professor of statistics at the University Paris XI.