All listings for this product
Best-selling in Non-Fiction Books
Save on Non-Fiction Books
- AU $46.25Trending at AU $49.54
- AU $3.75Trending at AU $7.40
- AU $41.64Trending at AU $45.32
- AU $27.04Trending at AU $31.95
- AU $70.89Trending at AU $79.84
- AU $20.50Trending at AU $24.15
- AU $22.60Trending at AU $23.10
About this product
- DescriptionMultivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model tation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model estimation, and endogeus variables, along with SabreR commands and examples. Improve Your Longitudinal Study In medical and social science research, MGLMMs help disentangle state dependence from incidental parameters. Focusing on these sophisticated data analysis techniques, this book explains the statistical theory and modeling involved in longitudinal studies. Many examples throughout the text illustrate the analysis of real-world data sets. Exercises, solutions, and other material are available on a supporting website.
- Author BiographyDamon M. Berridge is a senior lecturer in the Department of Mathematics and Statistics at Lancaster University. Dr. Berridge has nearly 20 years of experience as a statistical consultant. His research focuses on the modeling of binary and ordinal recurrent events through random effects models, with application in medical and social statistics. Robert Crouchley is a professor of applied statistics and director of the Centre for e-Science at Lancaster University. His research interests involve the development of statistical methods and software for causal inference in nonexperimental data. These methods include models for errors in variables, missing data, heterogeneity, state dependence, nonstationarity, event history data, and selection effects.
- Author(s)Damon Mark Berridge,Robert Crouchley
- PublisherTaylor & Francis Inc
- Date of Publication20/04/2011
- Place of PublicationBosa Roca
- Country of PublicationUnited States
- ImprintCRC Press Inc
- Content Note18 black & white illustrations, 9 black & white tables
- Weight544 g
- Width156 mm
- Height235 mm
- Spine20 mm
- Format DetailsUnsewn / adhesive bound
This item doesn't belong on this page.
Thanks, we'll look into this.