Model Averaging

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Beschreibung

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.



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



Autorentext

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. 




Inhalt
Why Model Averaging?.- Bayesian Model Averaging.- Frequentist Model Averaging.- Summary and Future Directions.

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Produktinformationen

Titel
Model Averaging
Autor
EAN
9783662585405
ISBN
978-3-662-58540-5
Format
Kartonierter Einband
Herausgeber
Springer, Berlin
Genre
Mathematik
Anzahl Seiten
107
Gewicht
222g
Größe
H5mm x B7mm x T156mm
Jahr
2019
Untertitel
Englisch
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