Statistical Modelling in R

Erscheinungsjahr 2009
ISBN: 978-0-19-921913-1
Verlag: Oxford University Press

Buch, Englisch, 592 Seiten, Print PDF, Format (B × H): 156 mm x 233 mm, Gewicht: 877 g

R is now the most widely used statistical package/language in university statistics departments and many research organisations. Its great advantages are that for many years it has been the leading-edge statistical package/language and that it can be freely downloaded from the R web site. Its cooperative development and open code also attracts many contributors meaning that the modelling and data analysis possibilities in R are much richer than in GLIM4, and so the R edition can be substantially more comprehensive than the GLIM4 edition of Statistical Modelling.

This text provides a comprehensive treatment of the theory of statistical modelling in R with an emphasis on applications to practical problems and an expanded discussion of statistical theory. A wide range of case studies is provided, using the normal, binomial, Poisson, multinomial, gamma, exponential and Weibull distributions, making this book ideal for graduates and research students in applied statistics and a wide range of quantitative disciplines.

Autoren/Hrsg.

Aitkin, Murray

Francis, Brian

Hinde, John

Darnell, Ross

Fachgebiete

• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Inhaltsverzeichnis

- Preface

- 1: Introducing R

- 2: Statistical modelling and inference

- 3: Regression and analysis of variance

- 4: Binary response data

- 5: Multinomial and Poisson response data

- 6: Survival data

- 7: Finite mixture models

- 8: Random effects models

- 9: Variance component models

- Bibliography

- R function and constant index

- Dataset index

- Subject index

Über Autor(innen)

Murray Aitkin is a Professorial Fellow at the Department of Mathematics and Statistics, University of Melbourne. In 1992 he was awarded an ARC Senior Research Fellowship, initially at the Australian National University and then at the University of Western Australia, where he worked on foundational issues in statistics. At the conclusion of the fellowship he was appointed to the Chair of Statistics at the University of Newcastle, UK, from which he took early retirement in 2004. In 2000-2002 he held a consulting position as Chief Statistician at the Education Statistics Services Institute, a division of the American Institutes for Research which provided consultancy to the National Center for Education Statistics of the US Department of Education. He continued to work as a consultant for NCES after 2002 at Newcastle, and this continues in Melbourne.

John Hinde is Professor of Statistics at the National University of Ireland Galway having previously worked at the Universities of Exeter and Lancaster in the UK. It was while at Lancaster that he met his co-authors and wrote the original book Statistical Modelling in GLIM that was later revised and has now been translated to R. His interests are in all aspects of statistical modelling, including generalized linear models and their extensions, and statistical computing. Particular interests are in overdispersion modelling, mixture models, and random effect models. He was a joint founding editor of the journal Statistical Modelling and served as Chairman of the Statistical Modelling Society. He is currently President of the Irish Statistical Association.

Dr Darnell has in excess of 25 years experience working as a statistical consultant and statistics lecturer in universities in Australia and the UK as well as for research organisations. Currently with CSIRO's Division of Mathematical and Information Sciences as a senior applied statistician working on environmental statistics.