A Comparison of the Bayesian and Frequentist Approaches to Estimation
This monograph contributes to the area of comparative statistical inference. Attention is restricted to the important subfield of statistical estimation. The book is intended for an audience having a solid grounding in probability and statistics at the le
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Springer Series in Statistics
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Francisco J. Samaniego
A Comparison of the Bayesian and Frequentist Approaches to Estimation
Francisco J. Samaniego Department of Statistics University of California 1 Shields Avenue Davis, CA 95616 USA [email protected]
ISSN 0172-7397 ISBN 978-1-4419-5940-9 e-ISBN 978-1-4419-5941-6 DOI 10.1007/978-1-4419-5941-6 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2010929747 © Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Dedication To my family: Mary, my compass, for forty years of love and support; Monica and Elena, who have brought me nothing but love and enormous pride; Keb, for his friendship and contagious positive outlook; Jack and Will, for the joy they constantly bring to their Papa’s life; and my sister Margarita, whose constant encouragement, since we were toddlers, gave me the courage to dream impossible dreams and seek to make them a reality; and, To three friends who are primarily responsible for sparking my interests in Bayesian Statistics: Thomas Ferguson, my teacher and mentor in graduate school and beyond; Dennis Lindley, whose visit to Davis as a Regent’s Professor in the 1980s really rocked my boat; and Nozer Singpurwalla, whose creativity and generosity did much to sustain and expand these interests. One qualification: each of them should be absolved of any responsibility for the views and opinions put forward in this monograph.
Preface
The main theme of this monograph is “comparative statistical inference.” While the topics covered have been carefully selected (they are, for example, restricted to problems of statistical estimation), my aim is to provide ideas and examples which will assist a statistician, or a statistical practitioner, in comparing the performance one can expect from using either Bayesian or classical (aka, frequentist) solutions in estimation problems. Before investing the hours it will take to read this monograph, one might well want to know what sets it apart from other treatises on comparative inference. The two books that are closest to the present work are the well-known tomes by Barnett (1999) and Cox (2006).
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