Overview
- Provides an overview of current model averaging methods, with an emphasis on applications
- Compares the frequentist and Bayesian approaches to model averaging
- Includes an extensive list of references and suggestions for further research
Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)
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Table of contents (4 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This book provides a concise and accessible overview of model averaging, with a focus on applications. Model averaging is a common means of allowing for model uncertainty when analysing data, and has been used in a wide range of application areas, such as ecology, econometrics, meteorology and pharmacology. The book presents an overview of the methods developed in this area, illustrating many of them with examples from the life sciences involving real-world data. It also includes an extensive list of references and suggestions for further research. Further, it clearly demonstrates the links between the methods developed in statistics, econometrics and machine learning, as well as the connection between the Bayesian and frequentist approaches to model averaging. The book appeals to statisticians and scientists interested in what methods are available, how they differ and what is known about their properties. It is assumed that readers are familiar with the basic concepts of statistical theory and modelling, including probability, likelihood and generalized linear models.
Authors and Affiliations
About the author
David Fletcher is an Associate Professor of Statistics at the University of Otago in Dunedin, New Zealand. His research interests developed primarily from collaboration with other scientists, particularly ecologists. He has developed new methods in a range of areas, including experimental design, mark-recapture, meta-regression, model averaging, population dynamics, overdispersion and zero-inflated data.
Bibliographic Information
Book Title: Model Averaging
Authors: David Fletcher
Series Title: SpringerBriefs in Statistics
DOI: https://doi.org/10.1007/978-3-662-58541-2
Publisher: Springer Berlin, Heidelberg
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s), under exclusive licence to Springer-Verlag GmbH, DE, part of Springer Nature 2018
Softcover ISBN: 978-3-662-58540-5Published: 25 January 2019
eBook ISBN: 978-3-662-58541-2Published: 17 January 2019
Series ISSN: 2191-544X
Series E-ISSN: 2191-5458
Edition Number: 1
Number of Pages: X, 107
Number of Illustrations: 4 b/w illustrations
Topics: Statistical Theory and Methods, Theoretical Ecology/Statistics, Biostatistics, Statistics for Business, Management, Economics, Finance, Insurance, Statistics for Life Sciences, Medicine, Health Sciences