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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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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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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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Regression Modeling: Methods, Theory, and Computation with SAS

methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy;

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Regression Modeling: Methods, Theory, and Computation with SAS

methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy;

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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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Robust Regression

Robust Regression: Analysis and Applications characterizes robust estimators in terms of how much they weigh. Each observation discusses;

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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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Algorithms for Regression and Classification

The focus of this dissertation is on robust regression and classification in genetic association studies. In the context of robust;

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Nonlinear Regression Modeling

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Introductory Statistics

literature) to support the theoretical discussion. Emphasizes the regression model, including nonlinear and multiple regression. Topics covered;

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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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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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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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Distributionally Robust Learning

series of learning problems, including (i) distributionally robust linear regression; (ii) distributionally robust regression with group;

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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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Regression Analysis by Example

of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. The book now includes a new chapter on the;

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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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Modeling in Mathematics

representing noisy sounds with short-time sinusoids, robust Bayesian regularized estimation based on regression model and robust quadratic regression;

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Operator-Based Nonlinear Control Systems Design And Applicat

begins with an introduction that defines nonlinear systems. Next, it covers: * Robust right coprime factorization for nonlinear plants with;

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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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Applications of Robust Control to Nonlinear Systems

This book offers a three-step approach to generating a robust nonlinear controller: modeling, synthesis and robustness analysis. The;

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Robust Statistics

types of scale estimates, asymptotic minimax theory, regression, robust covariance, and robust design. In addition to an extended treatment;

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Robust Statistics

results of the last decade included in this 2nd edition include: fast deterministic robust regression, finite-sample robustness, robust;

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Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear System

This book reports on the latest advances in adaptive critic control with robust stabilization for uncertain nonlinear systems. Covering the;

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