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Generalized Linear Models and Correlated Data Methods

Offers a unified discussion of generalized linear models and correlated data methods Provides information suitable for graduate non;

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Generalized Linear Models

This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective;

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Generalized Linear and Nonlinear Models for Correlated Data

Edward F. Vonesh's "Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS" is devoted to the;

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Foundations of Linear and Generalized Linear Models

of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to;

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Correlated Data Analysis

formulation of joint models for correlated data. This enables the book to cover a broader range of data types than the traditional generalized linear;

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Generalized Linear and Nonlinear Models for Correlated Data

Edward F. Vonesh's Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS is devoted to the;

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Generalized, Linear, and Mixed Models

data, which now includes a discussion of generalized linear models, modern advances in longitudinal data analysis, and the use between and;

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Generalized Linear Models with Random Effects

. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood;

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Generalized Linear Mixed Models

focuses on data-driven processes and provides context for extending traditional linear model thinking to generalized linear mixed modeling. See;

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An Introduction to Generalized Linear Models

Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive;

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Generalized Additive Models

methods relax the linear assumption of many standard models and allow analysts to uncover structure in the data that might otherwise have been;

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Linear and Generalized Linear Mixed Models and Their Applications

This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. It presents an up-to;

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Linear and Generalized Linear Mixed Models and Their Applications

This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. It presents an up-to;

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Generalized Estimating Equations

Although powerful and flexible, the method of generalized linear models (GLM) is limited in its ability to accurately deal with;

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An Introduction to Generalized Additive Models with R

splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the;

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Introduction to General and Generalized Linear Models

Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models;

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An Introduction to Generalized Linear Models

of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers Normal, Poisson, and Binomial;

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An Introduction to Generalized Linear Models

of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers Normal, Poisson, and Binomial;

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Interaction Effects in Linear and Generalized Linear Models: Examples and Applications Using Stata

Offering a clear set of workable examples with data and explanations, Interaction Effects in Linear and Generalized Linear Models is a;

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Statistical Methods for Spatial Data Analysis

generalized linear mixed models for spatial data. It succinctly discusses Bayesian hierarchical models and concludes with reviews on simulating random;

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Statistical Methods for Spatial Data Analysis

-correlated errors and generalized linear mixed models for spatial data. It succinctly discusses Bayesian hierarchical models and concludes with;

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Multivariate Generalized Linear Mixed Models Using R

Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data;

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Confidence Intervals in Generalized Regression Models

regression model, generalized linear model (GLIM), logistic regression model, Poisson regression model, multinomial regression model, and Cox;

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Confidence Intervals in Generalized Regression Models

regression model, generalized linear model (GLIM), logistic regression model, Poisson regression model, multinomial regression model, and Cox;

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Mixed Effects Models for Complex Data

, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and;

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Mixed Effects Models for Complex Data

, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and;

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Generalized Linear Models

disciplines, Generalized Linear Models serves as ideal text, self-study guide, and reference.;

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