Model Averaging
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
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David Fletcher
Model Averaging
123
SpringerBriefs in Statistics
More information about this series at http://www.springer.com/series/8921
David Fletcher
Model Averaging
123
David Fletcher Department of Mathematics and Statistics University of Otago Dunedin, New Zealand
ISSN 2191-544X ISSN 2191-5458 (electronic) SpringerBriefs in Statistics ISBN 978-3-662-58540-5 ISBN 978-3-662-58541-2 (eBook) https://doi.org/10.1007/978-3-662-58541-2 Library of Congress Control Number: 2018964926 © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE, part of Springer Nature 2018 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer-Verlag GmbH, DE part of Springer Nature The registered company address is: Heidelberger Platz 3, 14197 Berlin, Germany
To Sonya, for being there…
Preface
In writing this book, my aim has been to provide a succinct, accessible account of model averaging that will be useful to applied statisticians and scientists. I have emphasised the links between methods developed in statistics, econometrics and machine learning, as well as the interplay between the frequentist and Bayesian approaches. I have assumed that the reader is familiar with basic statistical theory and modelling, including probability, likelihood and generalised linear models. The references should help the reader follow up on topics I have not covered in detail. The number of papers written on model averaging is far greater than I had expected when starting this book, and I apologise in advance if I have overlooked any important articles. I have deliberately chosen small examples to illustrate the different methods, in order to facilitate the discussion of key concepts. Many applications of model averaging will be in more complex settings, but translation of the ideas to those settings will often
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