Asymptotic Statistics

Front Cover
Cambridge University Press, Jun 19, 2000 - Mathematics
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 research in asymptotic statistics.


Stochastic Convergence
Delta Method
Local Asymptotic Normality
Efficiency of Estimators
Limits of Experiments
Efficiency of Tests
Likelihood Ratio Tests
ChiSquare Tests
Empirical Processes
Functional Delta Method
Quantiles and Order Statistics

Bayes Procedures
Rank Sign and Permutation Statistics
Relative Efficiency of Tests
Nonparametric Density Estimation
Semiparametric Models

Other editions - View all

Common terms and phrases

Bibliographic information