Generalized Linear Models With Examples in R by Peter K. Dunn
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Artikelmerkmale
- Artikelzustand
- Book Title
- Generalized Linear Models With Examples in R
- Publish Year
- 2018
- Edition
- 1
- ISBN
- 9781441901170
Über dieses Produkt
Product Identifiers
Publisher
Springer New York
ISBN-10
1441901175
ISBN-13
9781441901170
eBay Product ID (ePID)
28038840005
Product Key Features
Number of Pages
Xx, 562 Pages
Language
English
Publication Name
Generalized Linear Models with Examples in R
Subject
Programming Languages / General, Mathematical & Statistical Software, Probability & Statistics / General, General
Publication Year
2018
Type
Textbook
Subject Area
Mathematics, Computers
Series
Springer Texts in Statistics Ser.
Format
Hardcover
Dimensions
Item Weight
36.4 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Reviews
"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), "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), "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)
Number of Volumes
1 vol.
Illustrated
Yes
Table Of Content
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.
Synopsis
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, 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., *This book eases students into GLMs and motivates the need for GLMs by starting with regression.* A practical working knowledge of good applied statistical practice is developed through the use of these real data sets and numerous case studies*. Each example in the text is cross-referenced with the relevant data set so that readers can load this data to follow the analysis in their own R session., 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
LC Classification Number
QA276-280
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