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Robust Nonlinear Regression

The first book to discuss robust aspects of nonlinear regression-with applications using R software Robust Nonlinear Regression: with;

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Nonlinear Regression with R

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

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Statistical Regression Modeling with R

This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear;

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Statistical Regression Modeling with R

This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear;

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Nonlinear Statistical Models

models, multivariate regression models, and simultaneous equation models. Methods are illustrated with worked examples, complete with figures;

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Nonlinear Regression Analysis and Its Applications

have put together an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models;

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Handbook of Regression Methods

provides a broad overview of regression models, diagnostic procedures, and inference procedures, with emphasis on how these methods are applied;

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Adaptive Regression for Modeling Nonlinear Relationships

when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for;

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Partially Linear Models

art of partially linear regression techniques. The emphasis is on methodologies rather than on the theory, with a particular focus on;

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Limit Theorems For Nonlinear Cointegrating Regression

This book provides the limit theorems that can be used in the development of nonlinear cointegrating regression. The topics include weak;

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Dose-Response Analysis Using R

-response data. The statistical models used are partly special cases, partly extensions of nonlinear regression models, generalized linear and;

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Dose-Response Analysis Using R

to illustrate the methodology. R code is integrated into the text to give guidance on applying the methods. Written with minimal mathematics;

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Nonlinear Regression Modeling

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Applied Regression Analysis

and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate;

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Bayesian Methods for Nonlinear Classification and Regression

Classification and Regression is the first book to bring together, in a consistent statistical framework, the ideas of nonlinear modelling and Bayesian;

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Alternative Methods of Regression

Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts .an extraordinary;

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Regression Modeling: Methods, Theory, and Computation with SAS

Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques;

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Regression Modeling: Methods, Theory, and Computation with SAS

Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques;

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Nonlinear Regression

of current theoretical literature, requiring only familiarity with linear regression methods. The three chapters on central computational questions;

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Conventional and Fuzzy Regression

deals with both crisp (conventional) linear or nonlinear regression and fuzzy linear or nonlinear regression. The application example refers to;

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Advanced Regression Models with SAS and R

Advanced Regression Models with SAS and R exposes the reader to the modern world of regression analysis. The material covered by this book;

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Advanced Regression Models with SAS and R

Advanced Regression Models with SAS and R exposes the reader to the modern world of regression analysis. The material covered by this book;

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Radiation Risk Estimation

covers nonlinear measurement error models, with a particular emphasis on efficiency of regression parameter estimators. In the second part, risk;

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Regression Analysis by Example

can be carried out with most of the currently available statistical software packages, such as the software package R. Regression Analysis by;

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R In Finance And Economics

This book provides an introduction to the statistical software R and its application with an empirical approach in finance and economics;

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Applied Regression and Modeling

regression analysis with applications, and (3) regression and modeling including the second order models, nonlinear regression, and interaction;

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An R Companion to Applied Regression

An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied;

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