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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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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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Nonlinear Regression Modeling

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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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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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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

In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing;

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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

, nonlinear least squares, and time-series regression issues. For most of the regression methods, the author includes SAS procedure code, enabling;

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Regression Modeling: Methods, Theory, and Computation with SAS

, nonlinear least squares, and time-series regression issues. For most of the regression methods, the author includes SAS procedure code, enabling;

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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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Nonlinear Regression

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

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Confidence Intervals in Generalized Regression Models

a number of different regression models, from very simple to more complex ones. It covers the general linear model (GLM), nonlinear;

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Confidence Intervals in Generalized Regression Models

a number of different regression models, from very simple to more complex ones. It covers the general linear model (GLM), nonlinear;

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Introductory Statistics for Business and Economics

the use of computers. The regression model is well covered, including both nonlinear and multiple regression. The chapters contain many real;

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Nonlinear Statistical Models

. The three major categories of statistical models that relate dependent variables to explanatory variables are covered: univariate regression;

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Data Analysis Using Regression & Multi

researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety;

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Applied Numerical Methods for Chemical Engineers

equations, boundary value problems, partial differential equations, and linear and nonlinear regression analysis. MATLAB is adopted as the;

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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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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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Handbook of Regression Methods

diverse range of topics, including censored regression, nonlinear regression, generalized linear models, and semiparametric regression;

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Student Workbook to accompany Introductory Statistics for Business and Economics 4e and Introductory Statistics 5e

the use of computers. The regression model is well covered, including both nonlinear and multiple regression. The chapters contain many real;

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Data Analysis Using Regression and Multilevel/Hierarchical Models

perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the;

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Regression Models as a Tool in Medical Research

While regression models have become standard tools in medical research, understanding how to properly apply the models and interpret the;

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