Hardin / Hilbe Generalized Estimating Equations, Second Edition

2. Auflage 2013
ISBN: 978-1-4398-8114-9
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 277 Seiten

Generalized Estimating Equations, Second Edition updates the best-selling previous edition, which has been the standard text on the subject since it was published a decade ago. Combining theory and application, the text provides readers with a comprehensive discussion of GEE and related models. Numerous examples are employed throughout the text, along with the software code used to create, run, and evaluate the models being examined. Stata is used as the primary software for running and displaying modeling output; associated R code is also given to allow R users to replicate Stata examples. Specific examples of SAS usage are provided in the final chapter as well as on the book’s website.

This second edition incorporates comments and suggestions from a variety of sources, including the Statistics.com course on longitudinal and panel models taught by the authors. Other enhancements include an examination of GEE marginal effects; a more thorough presentation of hypothesis testing and diagnostics, covering competing hierarchical models; and a more detailed examination of previously discussed subjects.

Along with doubling the number of end-of-chapter exercises, this edition expands discussion of various models associated with GEE, such as penalized GEE, cumulative and multinomial GEE, survey GEE, and quasi-least squares regression. It also offers a thoroughly new presentation of model selection procedures, including the introduction of an extension to the QIC measure that is applicable for choosing among working correlation structures.

See Professor Hilbe discuss the book.

Zielgruppe

Researchers and graduate students in statistics, biostatistics, medicine, epidemiology, and the social sciences.

Autoren/Hrsg.

Hardin, James W.

Hilbe, Joseph M.

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik

Inhaltsverzeichnis

Introduction

Notational Conventions and Acronyms
A Short Review of Generalized Linear Models

Software

Exercises

Model Construction and Estimating Equations
Independent Data

Estimating the Variance of the Estimates

Panel Data

Estimation

Summary

Exercises

R code for Selected Output

Generalized Estimating Equations
Population-Averaged (PA) and Subject-Specific (SS) Models

The PA-GEE for GLMs

The SS-GEE for GLMs

The GEE2 for GLMs

GEEs for Extensions of GLMs

Further Developments and Applications

Missing Data

Choosing an Appropriate Model

Summary

Exercises

R Code for Selected Output

Residuals, Diagnostics, and Testing
Criterion Measures

Analysis of Residuals

Deletion Diagnostics

Goodness of Fit (Population-Averaged Models)

Testing Coefficients in the PA-GEE Model

Assessing the MCAR Assumption of PA-GEE Models

Summary

Exercises

Programs and Datasets

Programs

Datasets

References

Author Index

Subject Index

Über Autor(innen)

James W. Hardin is the Division Director of Biostatistics and an associate professor in the Department of Epidemiology and Biostatistics at the University of South Carolina. He is also an affiliated faculty in the Institute for Families in Society. Professor Hardin was the initial author of Stata’s xtgee command and has authored numerous articles and software applications related to GEE and associated models. Professor Hilbe and he have authored three editions of the popular Generalized Linear Models and Extensions and co-authored Stata’s current glm command. He has also co-authored (with P. Good) four editions of the well-accepted Common Errors in Statistics (and How to Avoid Them).
Joseph M. Hilbe is a Solar System Ambassador with the Jet Propulsion Laboratory, an adjunct professor of statistics at Arizona State University, and an Emeritus Professor at the University of Hawaii. An elected fellow of the American Statistical Association and elected member of the International Statistical Institute (ISI), Professor Hilbe is president of the International Astrostatistics Association as well as chair of the ISI Sports Statistics and Astrostatistics committees. He has authored two editions of the bestseller Negative Binomial Regression, Logistic Regression Models, and Astrostatistical Challenges for the New Astronomy. He has also co-authored Methods of Statistical Model Estimation (with A. Robinson), Quasi-Least Squares Regression (with J. Shults), and R for Stata Users (with R. Muenchen).