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This volume contains a selection of papers presented at the Fifth Franco-Belgian Meeting of Statisticians, held in Luminy-Marseille (France) on November 23-24, 1984. The diversity of these papers reflects the broadness of the topic of the meeting : the asymptotic theory for non i.i.d. processes. First of all, asymptotic theory is focused on various types of convergence : almost sure convergence, convergence in distribution and convergence in variation. In an other direction, relaxing the hypothesis of i.i.d. processes leads to consider a large variety of situations, characterized either by hypotheses on the marginal model (i.e. after integration with respect to parameters or exogenous variables) such as stationarity, exchangeability of Markovian property or by assumptions on the model conditionally on exogenoous variables. The main tools used in such situations are martingale theory and the ergodic theorem. They may be applied in various situations such as posterior expectations in Bayesian analysis, rational expectations, generalized residuals and mixing conditions in conditional models or predictions in nonstationary q-dependent processes. All the above concepts are met both theoretically and through applications in the present volume.
This unique book delivers an encyclopedic treatment of classic as well as contemporary large sample theory, dealing with both statistical problems and probabilistic issues and tools. The book is unique in its detailed coverage of fundamental topics. It is written in an extremely lucid style, with an emphasis on the conceptual discussion of the importance of a problem and the impact and relevance of the theorems. There is no other book in large sample theory that matches this book in coverage, exercises and examples, bibliography, and lucid conceptual discussion of issues and theorems.
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.
The series is devoted to the publication of high-level monographs and surveys which cover the whole spectrum of probability and statistics. The books of the series are addressed to both experts and advanced students.
This book is intended to provide a somewhat more comprehensive and unified treatment of large sample theory than has been available previously and to relate the fundamental tools of asymptotic theory directly to many of the estimators of interest to econometricians. In addition, because economic data are generated in a variety of different contexts (time series, cross sections, time series--cross sections), we pay particular attention to the similarities and differences in the techniques appropriate to each of these contexts.