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＜E-Book＞
Model Averaging / by David Fletcher
(SpringerBriefs in Statistics. ISSN:21915458)

Edition	1st ed. 2018.
Publisher	Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer
Year	2018
Size	X, 107 p. 4 illus : online resource
Authors	*Fletcher, David author SpringerLink (Online service)
Subjects	LCSH:Statistics  LCSH:Ecology  LCSH:Biometry FREE:Statistical Theory and Methods FREE:Theoretical and Statistical Ecology FREE:Biostatistics FREE:Statistics in Business, Management, Economics, Finance, Insurance
Notes	Why Model Averaging? -- Bayesian Model Averaging -- Frequentist Model Averaging -- Summary and Future Directions 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 HTTP:URL=https://doi.org/10.1007/978-3-662-58541-2

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