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xv, 443 p. : ill. ; 26 cm.
  • 1. Introduction-- 2. Stochastic convergence-- 3. The delta-method-- 4. Moment estimators-- 5. M- and Z-estimators-- 6. Contiguity-- 7. Local asymptotic normality-- 8. Efficiency of estimators-- 9. Limits of experiments-- 10. Bayes procedures-- 11. Projections-- 12. U-statistics-- 13. Rank, sign, and permutation statistics-- 14. Relative efficiency of tests-- 15. Efficiency of tests-- 16. Likelihood ratio tests-- 17. Chi-square tests-- 18. Stochastic convergence in metric spaces-- 19. Empirical processes-- 20. The functional delta-method-- 21. Quantiles and order statistics-- 22. L-statistics-- 23. The bootstrap-- 24. Nonparametric density estimation-- 25. Semiparametric models.
  • (source: Nielsen Book Data)9780521496032 20160528
This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. This entails mainly the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation. Thus, even the standard subjects of asymptotic statistics are presented in a novel way. Suitable as a graduate or Master's level statistics text, this book will also give researchers an overview of the latest research in asymptotic statistics.
(source: Nielsen Book Data)9780521496032 20160528
Cambridge Core Access limited to one user.
Science Library (Li and Ma)
xxvi, 589 p. : ill. ; 25 cm.
  • Preface to the Second Edition.- Preface to the First Edition.- List of Tables.- List of Figures.- List of Examples.- Table of Notation.- Preparations.- Unbiasedness.- Equivariance.- Average Risk Optimality.- Minimaxity and Admissibility.- Asymptotic Optimality.- References.- Author Index.- Subject Index.
  • (source: Nielsen Book Data)9780387985022 20160528
This second, much enlarged edition by Lehmann and Casella of Lehmann's classic text on point estimation maintains the outlook and general style of the first edition. All of the topics are updated. An entirely new chapter on Bayesian and hierarchical Bayesian approaches is provided, and there is much new material on simultaneous estimation. Each chapter concludes with a Notes section which contains suggestions for further study. The book is a companion volume to the second edition of Lehmann's "Testing Statistical Hypotheses".E.L. Lehmann is Professor Emeritus at the University of California, Berkeley. He is a member of the National Academy of Sciences and the American Academy of Arts and Sciences, and the recipient of honorary degrees from the University of Leiden, The Netherlands, and the University of Chicago. George Casella is the Liberty Hyde Bailey Professor of Biological Statistics in The College of Agriculture and Life Sciences at Cornell University. Casella has served as associate editor of The American Statistician, Statistical Science and JASA. He is currently the Theory and Methods Editor of "JASA". Casella has authored two other textbooks ("Statistical Inference", 1990, with Roger Berger and "Variance Components", 1992, with Shayle A. Searle and Charles McCulloch). He is a fellow of the IMS and ASA, and an elected fellow of the ISI. Also available bu E.L. Lehmann is, "Testing Statistical Hypotheses, Second Edition", Springer- Verlag New York, Inc., ISBN 0-387-94919-4.
(source: Nielsen Book Data)9780387985022 20160528
Science Library (Li and Ma)