Synopsis
Here is a practical and mathematically rigorous introduction to the field of asymptotic statistics. In addition to most of the standard topics of an asymptotics course--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, one of the book's unifying themes that mainly entails the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation.
Book Description
This book is an introduction to the field of asymptotic statistics. The treatment is mathematically rigorous but practical rather than simply technical. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. In addition to the usual topics of an asymptotics course, the author also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes. Suitable as a text for a graduate or Master's level statistics course, this book will also serve as a reference for researchers in statistics, probability, and their applications.
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