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Mixed Effects Models for Complex Data

studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate;

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Mixed Effects Models for Complex Data

studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate;

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Asymptotic Analysis of Mixed Effects Models

linear mixed models, non-linear mixed effects models, and non-parametric mixed effects models are complex models, yet, these models are;

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Asymptotic Analysis of Mixed Effects Models

linear mixed models, non-linear mixed effects models, and non-parametric mixed effects models are complex models, yet, these models are;

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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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Mixed-Effects Models in S and S-PLUS

software, and theory makes the book a useful reference for practitioners who use, or intend to use, mixed-effects models in their data analyses. It;

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Mixed-Effects Models in S and S-PLUS

software, and theory makes the book a useful reference for practitioners who use, or intend to use, mixed-effects models in their data analyses. It;

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Generalized, Linear, and Mixed Models

style and wealth of illustrative exercises, Generalized, Linear, and Mixed Models , Second Edition is an ideal book for courses on generalized;

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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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Statistical Tests for Mixed Linear Models

treatment for these models at an advanced level. Statistical Tests for Mixed Linear Models:* Combines analysis and testing in one self-contained;

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Linear and Generalized Linear Mixed Models and Their Applications

who are interested in using mixed models for statistical data analysis.;

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Linear and Generalized Linear Mixed Models and Their Applications

who are interested in using mixed models for statistical data analysis.;

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Generalized Linear Mixed Models

normally distributed data, this one adopts a generalized mixed model approach throughout: data for linear modeling need not be normally distributed;

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Linear Mixed Models

easily perform complex linear mixed model (LMM) analyses via a variety of software programs. Linear Mixed Models: A Practical Guide Using;

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SAS for Mixed Models, Second Edition

featuring the MIXED, GLIMMIX, and NLMIXED procedures in this valuable edition of the comprehensive mixed models guide for data analysis, completely;

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Mixed Effects Models for the Population Approach

Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects ModelsMixed Effects Models for the Population Approach;

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Mixed Effects Models for the Population Approach

Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects ModelsMixed Effects Models for the Population Approach;

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Handbook of Mixed Membership Models and Their Applications

In response to scientific needs for more diverse and structured explanations of statistical data, researchers have discovered how to model;

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Handbook of Mixed Membership Models and Their Applications

In response to scientific needs for more diverse and structured explanations of statistical data, researchers have discovered how to model;

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Richly Parameterized Linear Models

A First Step toward a Unified Theory of Richly Parameterized Linear Models Using mixed linear models to analyze data often leads to;

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Richly Parameterized Linear Models

A First Step toward a Unified Theory of Richly Parameterized Linear Models Using mixed linear models to analyze data often leads to;

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Robust Mixed Model Analysis

of methods currently available that are robust in certain aspects. Learning about these methods is essential for the practice of mixed-effects;

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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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Linear Model Theory

A precise and accessible presentation of linear model theory, illustrated with data examples Statisticians often use linear models for data;

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Advanced R Statistical Programming and Data Models

Programming and Data Models shows you how to conduct data analysis using the popular R language. You'll delve into the preconditions or hypothesis for;

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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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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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