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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 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 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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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 Models

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

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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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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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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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A First Course in Linear Model Theory

selection, and missing data. A new chapter on generalized linear models (Chapter 12). A new section on multivariate linear models in Chapter 13;

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Generalized Additive Models

This book describes an array of power tools for data analysis that are based on nonparametric regression and smoothing techniques. These;

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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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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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Data Analysis Using Hierarchical Generalized Linear Models with R

advanced statistical modelling including generalized linear models with random effects, survival analysis and frailty models, multivariate HGLMs;

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Data Analysis Using Hierarchical Generalized Linear Models with R

advanced statistical modelling including generalized linear models with random effects, survival analysis and frailty models, multivariate HGLMs;

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Pharmacokinetic-Pharmacodynamic Modeling and Simulation

considerably expands with the addition of three new chapters relating to Bayesian models, Generalized linear and nonlinear mixed effects models, and;

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Generalized Linear Models

adapt their data for this type of analysis. Examples are provided to illustrate the application of GLM to actual data and the author includes;

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An Introduction to Generalized Linear Models

focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models;

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Generalized Linear Models

Explaining the theoretical underpinning of generalized linear models, this text enables researchers to decide how to select the best way to;

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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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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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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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Methods and Applications of Longitudinal Data Analysis

Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical;

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