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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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A First Course in Linear Model Theory

. Revised or expanded topics include linear fixed, random and mixed effects models, generalized linear models, Bayesian and hierarchical linear;

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Generalized Linear Mixed Models

Generalized Linear Mixed Models: Modern Concepts, Methods and Applications presents an introduction to linear modeling using the;

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Advanced R Statistical Programming and Data Models

linear models, generalized additive models, mixed effects models, machine learning, and parallel processing Carry out regression modeling using R;

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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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Data Analysis Using Hierarchical Generalized Linear Models with R

Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects;

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Data Analysis Using Hierarchical Generalized Linear Models with R

Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects;

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Extending The Linear Model With R

expanded both in popularity and in the number of packages available. Extending the Linear Model with R: Generalized Linear, Mixed Effects and;

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An Introduction to Generalized Additive Models with R

Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and;

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Multilevel and Longitudinal Modeling Using Stata, Volumes I and II, Third Edition

This book examines Stata's treatment of generalized linear mixed models, also known as multilevel or hierarchical models. These models are;

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Multivariate, Multilinear and Mixed Linear Models

This book presents the latest findings on statistical inference in multivariate, multilinear and mixed linear models, providing a holistic;

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Introduction to General and Generalized Linear Models

necessary likelihood theory, the book covers both general and generalized linear models using the same likelihood-based methods. It presents the;

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Linear Model Theory

linear models separately, whereas Linear Model Theory: Univariate, Multivariate, and Mixed Models presents a unified treatment in order to make;

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Linear Algebra and Linear Models

basic aspects of the theory of linear estimation and hypothesis testing. The emphasis is on the approach using generalized inverses. Topics such;

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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 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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Robust Nonparametric Statistical Methods

univariate tests and estimates with extensions to linear models, multivariate models, times series models, experimental designs, and mixed models. It;

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Multilevel and Longitudinal Modeling Using Stata, Volume I

continuous outcomes, make it straightforward to transition to generalized linear mixed models for noncontinuous outcomes described in volume II.;

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Generalized, Linear, and Mixed Models

An accessible and self-contained introduction to statistical models-now in a modernized new edition Generalized, Linear, and Mixed Models;

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Asymptotic Analysis of Mixed Effects Models

Large sample techniques are fundamental to all fields of statistics. Mixed effects models, including linear mixed models, generalized;

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Asymptotic Analysis of Mixed Effects Models

Large sample techniques are fundamental to all fields of statistics. Mixed effects models, including linear mixed models, generalized;

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Generalized Additive Models

the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment;

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Extending the Linear Model with R

diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and;

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Pharmacokinetic-Pharmacodynamic Modeling and Simulation

developing the field by starting from linear models and then moving up to generalized linear and non-linear mixed effects models. Since the first;

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Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials

Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials is the first book focused on the application;

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Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials

Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials is the first book focused on the application;

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