Optimal Estimating Function Theory
Classical statistical inference emphasizes unbiased estimators rather than unbiased estimating equations.
- PDF / 6,349,665 Bytes
- 626 Pages / 453.543 x 683.15 pts Page_size
- 2 Downloads / 205 Views
Jing Qin
Biased Sampling, Over-identified Parameter Problems and Beyond
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
Jing Qin
Biased Sampling, Over-identified Parameter Problems and Beyond
123
Jing Qin Biostatistics Research Branch National Institute of Allergy and Infectious Diseases Bethesda, MD USA
ISSN 2199-0980 ICSA Book Series in Statistics ISBN 978-981-10-4854-8 DOI 10.1007/978-981-10-4856-2
ISSN 2199-0999
(electronic)
ISBN 978-981-10-4856-2
(eBook)
Library of Congress Control Number: 2017939898 © 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. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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, #21-01/04 Gateway East, Singapore 189721, Singapore
Preface
When I was a graduate student more than twenty five years ago, I was struggling to read many statistical research papers. This is particularly true at the time when I had passed my Ph.D. qualification examination. The goal of this book is to make it easier for Ph.D. students and new researchers to embark in their research area. During the past 30 years, statistics has become more an applied and more diversified science. In response to this trend, I have tried to cover as many different topics as possible. My main research interest focuses on likelihood-based inferences, which includes parametric likelihood, biased sampling likelihood, semiparametric likelihood, empirical likelihood and Godambe’s estimating function theory. This book is devoted to biased sampling problems (also call
Data Loading...
