Elements of Frequentist Inference
This chapter discusses fundamental concepts of frequentist inference, such as unbiasedness and consistency, standard errors and confidence intervals, significance tests and P-values. There is also a section on the bootstrap method. Exercises are given at
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Applied Statistical Inference Likelihood and Bayes
Applied Statistical Inference
Leonhard Held r Daniel Sabanés Bové
Applied Statistical Inference Likelihood and Bayes
Leonhard Held Institute of Social and Preventive Medicine University of Zurich Zurich, Switzerland
Daniel Sabanés Bové Institute of Social and Preventive Medicine University of Zurich Zurich, Switzerland
ISBN 978-3-642-37886-7 ISBN 978-3-642-37887-4 (eBook) DOI 10.1007/978-3-642-37887-4 Springer Heidelberg New York Dordrecht London Library of Congress Control Number: 2013954443 Mathematics Subject Classification: 62-01, 62F10, 62F12, 62F15, 62F25, 62F40, 62P10, 65C05, 65C60 © Springer-Verlag Berlin Heidelberg 2014 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. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. 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. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
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Preface
Statistical inference is the science of analysing and interpreting data. It provides essential tools for processing information, summarizing the amount of knowledge gained and quantifying the remaining uncertainty. This book provides an introduction to the principles and concepts of the two most commonly used methods in scientific investigations: Likelihood and Bayesian inference. The two approaches are usually seen as competing paradigms, but we
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