Model Averaging (eBook)
X, 107 Seiten
Springer Berlin Heidelberg (Verlag)
978-3-662-58541-2 (ISBN)
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.
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.
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.
Why Model Averaging?.- Bayesian Model Averaging.- Frequentist Model Averaging.- Summary and Future Directions.
Erscheint lt. Verlag | 17.1.2019 |
---|---|
Reihe/Serie | SpringerBriefs in Statistics | SpringerBriefs in Statistics |
Zusatzinfo | X, 107 p. 4 illus. |
Verlagsort | Berlin |
Sprache | englisch |
Themenwelt | Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik |
Medizin / Pharmazie ► Allgemeines / Lexika | |
Naturwissenschaften ► Biologie | |
Wirtschaft | |
Schlagworte | Applications of model averaging in ecology • Bayesian modeling • Frequentist Model Averaging • mixed models • Model Averaging • Posterior model probabilities |
ISBN-10 | 3-662-58541-3 / 3662585413 |
ISBN-13 | 978-3-662-58541-2 / 9783662585412 |
Haben Sie eine Frage zum Produkt? |
Größe: 2,2 MB
DRM: Digitales Wasserzeichen
Dieses eBook enthält ein digitales Wasserzeichen und ist damit für Sie personalisiert. Bei einer missbräuchlichen Weitergabe des eBooks an Dritte ist eine Rückverfolgung an die Quelle möglich.
Dateiformat: PDF (Portable Document Format)
Mit einem festen Seitenlayout eignet sich die PDF besonders für Fachbücher mit Spalten, Tabellen und Abbildungen. Eine PDF kann auf fast allen Geräten angezeigt werden, ist aber für kleine Displays (Smartphone, eReader) nur eingeschränkt geeignet.
Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen dafür einen PDF-Viewer - z.B. den Adobe Reader oder Adobe Digital Editions.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen dafür einen PDF-Viewer - z.B. die kostenlose Adobe Digital Editions-App.
Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.
aus dem Bereich