Nonparametric Bayesian Inference in Biostatistics
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in pro
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Riten Mitra Peter Müller Editors
Nonparametric Bayesian Inference in Biostatistics
Frontiers in Probability and the Statistical Sciences
Editor-in Chief: Somnath Datta Department of Bioinformatics & Biostatistics University of Louisville Louisville, Kentucky, USA Series Editors: Frederi G. Viens Department of Mathematics & Department of Statistics Purdue University West Lafayette, Indiana, USA Dimitris N. Politis Department of Mathematics University of California, San Diego La Jolla, California, USA Hannu Oja Department of Mathematics and Statistics University of Turku Turku, Finland Michael Daniels Section of Integrative Biology Division of Statistics & Scientific Computation University of Texas Austin, Texas, USA
More information about this series at http://www.springer.com/series/11957
Riten Mitra • Peter M¨uller Editors
Nonparametric Bayesian Inference in Biostatistics
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Editors Riten Mitra Department of Bioinformatics and Biostatistics University of Louisville Louisville, KY, USA
Peter M¨uller Department of Mathematics University of Texas Austin, TX, USA
Frontiers in Probability and the Statistical Sciences ISBN 978-3-319-19517-9 ISBN 978-3-319-19518-6 (eBook) DOI 10.1007/978-3-319-19518-6 Library of Congress Control Number: 2015945621 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2015 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. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www. springer.com)
Preface
Nonparametric Bayesian (BNP) approaches are becoming increasingly more common in biostatistical inference. Many problems involve an abundance of data that allows the use of more flexible and complex probability models beyond traditional parametric families. One of the most traditional application areas for BNP is in survival analysis, including in particular survival regression. The nature of the recorded outcomes makes it natu
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