Statistical models.- Linear regression models.- Linear regression models: diagnostics and model-building.- Beyond linear regression: the method of maximum likelihood.- Generalized linear models: structure.- Generalized linear models: estimation.- Generalized linear models: inference.- Generalized linear models: diagnostics.- Models for proportions: binomial GLMs.- Models for counts: Poisson and negative binomial GLMs.- Positive continuous data: gamma and inverse Gaussian GLMs.- Tweedie GLMs.- Extra problems.- Appendix A: Using R for data analysis.- Appendix B: The GLMsData package.- Index: Data sets.- Index: R commands.- Index: General Topics.
Peter K. Dunn is Associate Professor in the Faculty of Science,
Health, Education and Engineering at the University of the Sunshine
Coast. His work focuses on mathematical statistics, in particular
generalized linear models. He has developed methods for accurate
numerical evaluation of the densities of the Tweedie distributions,
leading to a better understanding of these distributions. An
engaging teacher, Dunn is the recipient of an Australian Office of
Learning and Teaching citation. He has also won several conference
paper prizes, including the EJ Pitman Prize at the Australian
Statistics Conference. He is a member of the Statistical Society of
Australia Inc. and the Australian Mathematics Society.
Gordon K. Smyth is Head of the Bioinformatics Division at the Walter and Eliza Hall Institute of Medical Research and Honorary Professor of Mathematics & Statistics at The University of Melbourne. He has published research on generalized linear models and statistical computing for over 30 years and is the author of several popular R packages. In recent years, he has particularly promoted the use of generalized linear models to model data from genomic sequencing technologies.