Generalized Linear Mixed Models: Modern Concepts, Methods and Applications
Generalized Linear Mixed Models: Modern Concepts, Methods and Applications
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发表时间：
2012-09
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通讯作者：
W. Stroup
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作者：
W. Stroup
第一部分大图景建模基础什么是模型？两种模型形式：模型方程和模型效应的概率分布类型以矩阵形式编写模型摘要：完整陈述模型设计的基本要素重要性将设计和目标转换为描述“数据架构”的模型的介绍性想法，以促进从绘图平面到线性预测分布的模型规范重要性更复杂的示例：具有不同复制单位的多个因素设定模型推理的阶段目标：概述推理的基本工具问题I：数据规模与模型规模问题II：推理空间问题III：条件和边缘模型总结第二部分估计和推理要点估计介绍基本背景固定效应仅高斯混合模型广义线性混合模型总结推理，第一部分：模型效应介绍使用模型检验推理的基本背景方法-基于经验标准误差的统计推断主要思想和实施推断的一般指南摘要，第二部分：协方差分量介绍协方差分量的形式检验拟合统计量比较协方差模型区间估计摘要第三部分使用GLISH处理和解释性变量结构处理结构类型可估计函数类型多因素模型：概览包含所有因素的多因素模型定性多因素：一些因素定性，一些因素定量多因素：所有因素定量汇总多水平模型设计结构类型：单-和多水平模型定义多水平模型的类型以及它们如何在使用多水平设计的多水平模型中产生阻塞作用边际和条件多水平模型摘要最佳线性无偏预测仅随机效应模型中的可估计函数和可预测函数BLUP具有固定效应和随机效应的高斯数据复杂Z矩阵的高级应用摘要比率和比例比率和比例数据类型离散比例：二进制和二项数据用于二项数据的替代链接函数连续比例汇总计数简介计数数据中的过度离散替代分布条件和边缘太多零总结至事件发生时间数据简介：至事件发生时间数据的概率概念Gamma Glucose Glucose和生存分析总结多项数据概述有序类别的多项数据标称类别：广义Logit模型模型比较总结相关误差，第I部分：重复测量概述高斯数据：相关性和协方差模型的线性协方差模型选择非高斯情况下的问题非高斯重复测量汇总相关误差，第二部分：空间变异性概述高斯情况下的协方差模型通过平滑样条进行空间协方差建模非高斯情况汇总功效，样本量，基于GLMM的功效和精度分析的规划基础二项GLMM的高斯样本功效计数数据的基于GLMM的功效分析功效和重复测量的规划总结附录参考文献索引
PART I The Big Picture Modeling Basics What Is a Model? Two Model Forms: Model Equation and Probability Distribution Types of Model Effects Writing Models in Matrix Form Summary: Essential Elements for a Complete Statement of the Model Design Matters Introductory Ideas for Translating Design and Objectives into Models Describing "Data Architecture" to Facilitate Model Specification From Plot Plan to Linear Predictor Distribution Matters More Complex Example: Multiple Factors with Different Units of Replication Setting the Stage Goals for Inference with Models: Overview Basic Tools of Inference Issue I: Data Scale vs. Model Scale Issue II: Inference Space Issue III: Conditional and Marginal Models Summary PART II Estimation and Inference Essentials Estimation Introduction Essential Background Fixed Effects Only Gaussian Mixed Models Generalized Linear Mixed Models Summary Inference, Part I: Model Effects Introduction Essential Background Approaches to Testing Inference Using Model-Based Statistics Inference Using Empirical Standard Error Summary of Main Ideas and General Guidelines for Implementation Inference, Part II: Covariance Components Introduction Formal Testing of Covariance Components Fit Statistics to Compare Covariance Models Interval Estimation Summary PART III Working with GLMMs Treatment and Explanatory Variable Structure Types of Treatment Structures Types of Estimable Functions Multiple Factor Models: Overview Multifactor Models with All Factors Qualitative Multifactor: Some Factors Qualitative, Some Factors Quantitative Multifactor: All Factors Quantitative Summary Multilevel Models Types of Design Structure: Single- and Multilevel Models Defined Types of Multilevel Models and How They Arise Role of Blocking in Multilevel Models Working with Multilevel Designs Marginal and Conditional Multilevel Models Summary Best Linear Unbiased Prediction Review of Estimable and Predictable Functions BLUP in Random-Effects-Only Models Gaussian Data with Fixed and Random Effects Advanced Applications with Complex Z Matrices Summary Rates and Proportions Types of Rate and Proportion Data Discrete Proportions: Binary and Binomial Data Alternative Link Functions for Binomial Data Continuous Proportions Summary Counts Introduction Overdispersion in Count Data More on Alternative Distributions Conditional and Marginal Too Many Zeroes Summary Time-to-Event Data Introduction: Probability Concepts for Time-to-Event Data Gamma GLMMs GLMMs and Survival Analysis Summary Multinomial Data Overview Multinomial Data with Ordered Categories Nominal Categories: Generalized Logit Models Model Comparison Summary Correlated Errors, Part I: Repeated Measures Overview Gaussian Data: Correlation and Covariance Models for LMMs Covariance Model Selection Non-Gaussian Case Issues for Non-Gaussian Repeated Measures Summary Correlated Errors, Part II: Spatial Variability Overview Gaussian Case with Covariance Model Spatial Covariance Modeling by Smoothing Spline Non-Gaussian Case Summary Power, Sample Size, and Planning Basics of GLMM-Based Power and Precision Analysis Gaussian Example Power for Binomial GLMMs GLMM-Based Power Analysis for Count Data Power and Planning for Repeated Measures Summary Appendices References Index