Generalized Linear Models With Examples in R
(Sprache: Englisch)
Designed with teaching and learning in mind, this text eases readers into GLMs, beginning with regression. Its accessible content includes chapter summaries, exercises, short answers, clear examples, samples of R code, and the minimum necessary theory.
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Designed with teaching and learning in mind, this text eases readers into GLMs, beginning with regression. Its accessible content includes chapter summaries, exercises, short answers, clear examples, samples of R code, and the minimum necessary theory.
Klappentext zu „Generalized Linear Models With Examples in R “
This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics. Generalized linear models (GLMs) are powerful tools in applied statistics that extend the ideas of multiple linear regression and analysis of variance to include response variables that are not normally distributed. As such, GLMs can model a wide variety of data types including counts, proportions, and binary outcomes or positive quantities.The book is designed with the student in mind, making it suitable for self-study or a structured course. Beginning with an introduction to linear regression, the book also devotes time to advanced topics not typically included in introductory textbooks. It features chapter introductions and summaries, clear examples, and many practice problems, all carefully designed to balance theory and practice. The text also provides a working knowledge of applied statistical practice through the extensive use of R, which is integrated into the text.
Other features include:
- Advanced topics such as power variance functions, saddlepoint approximations, likelihood score tests, modified profile likelihood, small-dispersion asymptotics, and randomized quantile residuals
- Nearly 100 data sets in the companion R package GLMsData
- Examples that are cross-referenced to the companion data set, allowing readers to load the data and follow the analysis in their own R session
Inhaltsverzeichnis zu „Generalized Linear Models With Examples in R “
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.
Autoren-Porträt von Peter K. Dunn, Gordon K. Smyth
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.
Bibliographische Angaben
- Autoren: Peter K. Dunn , Gordon K. Smyth
- 2018, 1st ed. 2018, XX, 562 Seiten, Maße: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1441901175
- ISBN-13: 9781441901170
- Erscheinungsdatum: 11.11.2018
Sprache:
Englisch
Pressezitat
"This is a great book ... . The book comprehensively covers almost everything you need to know or teach in this area. This book is an invaluable reference either as a classroom text or for the researcher's bookshelf." (Pablo Emilio Verde, ISCB News, iscb.info, Issue 69, July, 2020)"I congratulate the authors for making an important contribution in this field. ... the book represents an excellent and very comprehensible introduction into the world of generalized linear models and is recommended for all readers who are looking for a practical introduction to this topic using R." (Dominic Edelmann, Biometrical Journal, Vol. 62, 2020)
"The book is targeted at students and notes it is appropriate for graduate students. It is also useful to the junior statistician needing to learn how to work a model they are unfamiliar with. The practicing and experienced statistician can use this as a quick reference for working a model they may have forgotten the specific of." (James P. Howard II, zbMath 1416.62020, 2019)
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