Generalized linear mixed models : modern concepts, methods and applications
QA279 .S78 2013
- Unknown QA279 .S78 2013
- Includes bibliographical references (pages 513-518) and index.
- 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.
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- Publisher's Summary
- Generalized Linear Mixed Models: Modern Concepts, Methods and Applications presents an introduction to linear modeling using the generalized linear mixed model (GLMM) as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how linear models fit with the rest of the core statistics curriculum and points out the major issues that statistical modelers must consider. Along with describing common applications of GLMMs, the text introduces the essential theory and main methodology associated with linear models that accommodate random model effects and non-Gaussian data. Unlike traditional linear model textbooks that focus on normally distributed data, this one adopts a generalized mixed model approach throughout: data for linear modeling need not be normally distributed and effects may be fixed or random. With numerous examples using SAS(R) PROC GLIMMIX, this book is ideal for graduate students in statistics, statistics professionals seeking to update their knowledge, and researchers new to the generalized linear model thought process. It focuses on data-driven processes and provides context for extending traditional linear model thinking to generalized linear mixed modeling. See Professor Stroup discuss the book.
(source: Nielsen Book Data)
- Linear models (Statistics)
- Publication date
- Copyright date
- Walter W. Stroup.
- Chapman & Hall/CRC texts in statistical science series
- "A Chapman & Hall book."