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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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Asymptotic Analysis of Mixed Effects Models

extensively used in practice. This monograph provides a comprehensive account of asymptotic analysis of mixed effects models. The monograph;

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Asymptotic Analysis of Mixed Effects Models

extensively used in practice. This monograph provides a comprehensive account of asymptotic analysis of mixed effects models. The monograph;

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Mixed Effects Models for Complex Data

approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews;

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Mixed Effects Models for Complex Data

approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews;

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Linear and Generalized Linear Mixed Models and Their Applications

approach to inference about non-Gaussian linear mixed models. Furthermore, it includes recently developed methods, such as mixed model diagnostics;

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Linear and Generalized Linear Mixed Models and Their Applications

approach to inference about non-Gaussian linear mixed models. Furthermore, it includes recently developed methods, such as mixed model diagnostics;

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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 Mixed Models

generalized linear mixed model (GLMM) as an overarching conceptual framework. For readers new to linear models, the book helps them see the big;

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Generalized, Linear, and Mixed Models

linear and mixed models at the upper-undergraduate and beginning-graduate levels. It also serves as a valuable reference for applied;

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Statistical Tests for Mixed Linear Models

compiles all currently available results in the area of exact and optimum tests for variance component models and offers the only comprehensive;

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Pharmacokinetic-Pharmacodynamic Modeling and Simulation

This is a second edition to the original published by Springer in 2006. The comprehensive volume takes a textbook approach systematically;

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SAS for Mixed Models, Second Edition

The indispensable, up-to-date guide to mixed models using SAS®. Discover the latest capabilities available for a variety of applications;

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Mixed-Effects Models in S and S-PLUS

concluding with diagnostic plots to assess the adequacy of a fitted model. The NLME library for analyzing mixed-effects models in S and S-PLUS;

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Mixed-Effects Models in S and S-PLUS

concluding with diagnostic plots to assess the adequacy of a fitted model. The NLME library for analyzing mixed-effects models in S and S-PLUS;

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Nonlinear Models for Repeated Measurement Data

unified development of methods and models for data of this type, with a detailed treatment of inference for the nonlinear mixed effects and its;

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

chapters. The chapters are organized in four parts. Volume II discusses generalized linear mixed models for binary, categorical, count, and;

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Robust Nonparametric Statistical Methods

follows the approach of the first edition by developing rank-based methods from the unifying theme of geometry. This edition, however, includes;

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Richly Parameterized Linear Models

better understand their analysis results. The author examines what is known and unknown about mixed linear models and identifies research;

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Richly Parameterized Linear Models

better understand their analysis results. The author examines what is known and unknown about mixed linear models and identifies research;

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A First Course in Linear Model Theory

. With an innovative approach, the authors introduce to students the mathematical and statistical concepts and tools that form a foundation for;

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Multilevel Modeling

providing a rationale for multilevel models; outlines the basic approach to estimating and evaluating a two-level model; discusses the major;

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Linear Mixed Models

comprehensive update of the available tools for fitting linear mixed-effects models in the newest versions of SAS, SPSS, R, Stata, and HLM. All examples;

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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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Statistical Methods for Drug Safety

selection bias in the analysis of large-scale longitudinal observational data. Choose the Right Statistical Approach for Analyzing Your Drug Safety;

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Statistical Methods for Drug Safety

selection bias in the analysis of large-scale longitudinal observational data. Choose the Right Statistical Approach for Analyzing Your Drug;

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