Monte-Carlo Simulation-Based Statistical Modeling
This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into t
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Ding-Geng (Din) Chen John Dean Chen Editors
Monte-Carlo SimulationBased Statistical Modeling
ICSA Book Series in Statistics Series editors Jiahua Chen, Department of Statistics, University of British Columbia, Vancouver, Canada Ding-Geng (Din) Chen, University of North Carolina, Chapel Hill, NC, USA
More information about this series at http://www.springer.com/series/13402
Ding-Geng (Din) Chen John Dean Chen •
Editors
Monte-Carlo Simulation-Based Statistical Modeling
123
Editors Ding-Geng (Din) Chen University of North Carolina Chapel Hill, NC USA
John Dean Chen Risk Management Credit Suisse New York, NY USA
and University of Pretoria Pretoria South Africa
ISSN 2199-0980 ICSA Book Series in Statistics ISBN 978-981-10-3306-3 DOI 10.1007/978-981-10-3307-0
ISSN 2199-0999
(electronic)
ISBN 978-981-10-3307-0
(eBook)
Library of Congress Control Number: 2016960187 © Springer Nature Singapore Pte Ltd. 2017 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 This Springer imprint is published by Springer Nature The registered company is Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #22-06/08 Gateway East, Singapore 189721, Singapore
Preface
Over the last two decades, advancements in computer technology have enabled accelerated research and development of Monte-Carlo computational methods. This book is a compilation of invited papers from some of the most forward-thinking statistical researchers. These authors present new developments in Monte-Carlo simulation-based statistical modeling, thereby creating an opportunity for the exchange ideas among researchers and users of statistical computing. Our aim in creating this book is to provide a venue for timely dissemination of the research in Monte-Carlo simulation-based statistical modeling to promote further research and collaborative work in this area. In the era of big data science, this collection of innovative research not only has remarkable potential
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