Asymptotics in Statistics

Asymptotics in Statistics
Author: Lucien Le Cam
Publisher: Springer Science & Business Media
Total Pages: 299
Release: 2012-12-06
Genre: Mathematics
ISBN: 1461211662

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This is the second edition of a coherent introduction to the subject of asymptotic statistics as it has developed over the past 50 years. It differs from the first edition in that it is now more 'reader friendly' and also includes a new chapter on Gaussian and Poisson experiments, reflecting their growing role in the field. Most of the subsequent chapters have been entirely rewritten and the nonparametrics of Chapter 7 have been amplified. The volume is not intended to replace monographs on specialized subjects, but will help to place them in a coherent perspective. It thus represents a link between traditional material - such as maximum likelihood, and Wald's Theory of Statistical Decision Functions -- together with comparison and distances for experiments. Much of the material has been taught in a second year graduate course at Berkeley for 30 years.

Asymptotic Statistics

Asymptotic Statistics
Author: A. W. van der Vaart
Publisher: Cambridge University Press
Total Pages: 470
Release: 2000-06-19
Genre: Mathematics
ISBN: 9780521784504

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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.

Asymptotics in Statistics

Asymptotics in Statistics
Author: Lucien Marie Le Cam
Publisher: Springer Science & Business Media
Total Pages: 202
Release: 1990
Genre: Mathematics
ISBN:

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This work presents a coherent introduction to asymptotic statistics. This Second Edition includes a new chapter on Gaussian and Poisson experiments because of their growing role in the field, especially in nonparametrics and semi-parametrics. Many chapters have been entirely rewritten and the nonparametrics material has been amplified.

Asymptotic Theory of Statistics and Probability

Asymptotic Theory of Statistics and Probability
Author: Anirban DasGupta
Publisher: Springer Science & Business Media
Total Pages: 726
Release: 2008-03-07
Genre: Mathematics
ISBN: 0387759700

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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.

Asymptotic Statistics

Asymptotic Statistics
Author: Reinhard Höpfner
Publisher: Walter de Gruyter GmbH & Co KG
Total Pages: 327
Release: 2014-05-26
Genre: Mathematics
ISBN: 3110367785

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This textbook is devoted to the general asymptotic theory of statistical experiments. Local asymptotics for statistical models in the sense of local asymptotic (mixed) normality or local asymptotic quadraticity make up the core of the book. Numerous examples deal with classical independent and identically distributed models and with stochastic processes. The book can be read in different ways, according to possibly different mathematical preferences of the reader. One reader may focus on the statistical theory, and thus on the chapters about Gaussian shift models, mixed normal and quadratic models, and on local asymptotics where the limit model is a Gaussian shift or a mixed normal or a quadratic experiment (LAN, LAMN, LAQ). Another reader may prefer an introduction to stochastic process models where given statistical results apply, and thus concentrate on subsections or chapters on likelihood ratio processes and some diffusion type models where LAN, LAMN or LAQ occurs. Finally, readers might put together both aspects. The book is suitable for graduate students starting to work in statistics of stochastic processes, as well as for researchers interested in a precise introduction to this area.

Asymptotic Methods in Statistical Decision Theory

Asymptotic Methods in Statistical Decision Theory
Author: Lucien Le Cam
Publisher: Springer Science & Business Media
Total Pages: 767
Release: 2012-12-06
Genre: Mathematics
ISBN: 1461249465

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This book grew out of lectures delivered at the University of California, Berkeley, over many years. The subject is a part of asymptotics in statistics, organized around a few central ideas. The presentation proceeds from the general to the particular since this seemed the best way to emphasize the basic concepts. The reader is expected to have been exposed to statistical thinking and methodology, as expounded for instance in the book by H. Cramer [1946] or the more recent text by P. Bickel and K. Doksum [1977]. Another pos sibility, closer to the present in spirit, is Ferguson [1967]. Otherwise the reader is expected to possess some mathematical maturity, but not really a great deal of detailed mathematical knowledge. Very few mathematical objects are used; their assumed properties are simple; the results are almost always immediate consequences of the definitions. Some objects, such as vector lattices, may not have been included in the standard background of a student of statistics. For these we have provided a summary of relevant facts in the Appendix. The basic structures in the whole affair are systems that Blackwell called "experiments" and "transitions" between them. An "experiment" is a mathe matical abstraction intended to describe the basic features of an observational process if that process is contemplated in advance of its implementation. Typically, an experiment consists of a set E> of theories about what may happen in the observational process.

Expansions and Asymptotics for Statistics

Expansions and Asymptotics for Statistics
Author: Christopher G. Small
Publisher: CRC Press
Total Pages: 359
Release: 2010-05-07
Genre: Mathematics
ISBN: 1420011022

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Asymptotic methods provide important tools for approximating and analysing functions that arise in probability and statistics. Moreover, the conclusions of asymptotic analysis often supplement the conclusions obtained by numerical methods. Providing a broad toolkit of analytical methods, Expansions and Asymptotics for Statistics shows how asymptoti

Robust Asymptotic Statistics

Robust Asymptotic Statistics
Author: Helmut Rieder
Publisher: Springer Science & Business Media
Total Pages: 409
Release: 2012-12-06
Genre: Mathematics
ISBN: 1468406248

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1 To the king, my lord, from your servant Balasi : 2 ... The king should have a look. Maybe the scribe who reads to the king did not understand . . . . shall I personally show, with this tablet that I am sending to the king, my lord, how the omen was written. 3 Really, he who has not followed the text with his finger cannot possibly understand it. This book is about optimally robust functionals and their unbiased esti mators and tests. Functionals extend the parameter of the assumed ideal center model to neighborhoods of this model that contain the actual distri bution. The two principal questions are (F): Which functional to choose? and (P): Which statistical procedure to use for the selected functional? Using a local asymptotic framework, we deal with both problems by linking up nonparametric statistical optimality with infinitesimal robust ness criteria. Thus, seemingly separate developments in robust statistics are presented in a unifying way.

Asymptotic Theory of Statistical Inference for Time Series

Asymptotic Theory of Statistical Inference for Time Series
Author: Masanobu Taniguchi
Publisher: Springer Science & Business Media
Total Pages: 671
Release: 2012-12-06
Genre: Mathematics
ISBN: 146121162X

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The primary aim of this book is to provide modern statistical techniques and theory for stochastic processes. The stochastic processes mentioned here are not restricted to the usual AR, MA, and ARMA processes. A wide variety of stochastic processes, including non-Gaussian linear processes, long-memory processes, nonlinear processes, non-ergodic processes and diffusion processes are described. The authors discuss estimation and testing theory and many other relevant statistical methods and techniques.

Asymptotics, Nonparametrics, and Time Series

Asymptotics, Nonparametrics, and Time Series
Author: Subir Ghosh
Publisher: CRC Press
Total Pages: 864
Release: 1999-02-18
Genre: Mathematics
ISBN: 9780824700515

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"Contains over 2500 equations and exhaustively covers not only nonparametrics but also parametric, semiparametric, frequentist, Bayesian, bootstrap, adaptive, univariate, and multivariate statistical methods, as well as practical uses of Markov chain models."