Asymptotic Theory of Statistics and Probability - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Asymptotic Theory of Statistics and Probability write by Anirban DasGupta. This book was released on 2008-03-07. Asymptotic Theory of Statistics and Probability available in PDF, EPUB and Kindle. 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 - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Asymptotic Statistics write by A. W. van der Vaart. This book was released on 2000-06-19. Asymptotic Statistics available in PDF, EPUB and Kindle. 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.
概率统计中的极限理论及其应用
概率统计中的极限理论及其应用 - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook 概率统计中的极限理论及其应用 write by . This book was released on 2007. 概率统计中的极限理论及其应用 available in PDF, EPUB and Kindle.
Asymptotic Theory of Statistical Inference for Time Series
Asymptotic Theory of Statistical Inference for Time Series - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Asymptotic Theory of Statistical Inference for Time Series write by Masanobu Taniguchi. This book was released on 2012-12-06. Asymptotic Theory of Statistical Inference for Time Series available in PDF, EPUB and Kindle. 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.
Statistical Estimation
Statistical Estimation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Statistical Estimation write by I.A. Ibragimov. This book was released on 2013-11-11. Statistical Estimation available in PDF, EPUB and Kindle. when certain parameters in the problem tend to limiting values (for example, when the sample size increases indefinitely, the intensity of the noise ap proaches zero, etc.) To address the problem of asymptotically optimal estimators consider the following important case. Let X 1, X 2, ... , X n be independent observations with the joint probability density !(x,O) (with respect to the Lebesgue measure on the real line) which depends on the unknown patameter o e 9 c R1. It is required to derive the best (asymptotically) estimator 0:( X b ... , X n) of the parameter O. The first question which arises in connection with this problem is how to compare different estimators or, equivalently, how to assess their quality, in terms of the mean square deviation from the parameter or perhaps in some other way. The presently accepted approach to this problem, resulting from A. Wald's contributions, is as follows: introduce a nonnegative function w(0l> ( ), Ob Oe 9 (the loss function) and given two estimators Of and O! n 2 2 the estimator for which the expected loss (risk) Eown(Oj, 0), j = 1 or 2, is smallest is called the better with respect to Wn at point 0 (here EoO is the expectation evaluated under the assumption that the true value of the parameter is 0). Obviously, such a method of comparison is not without its defects.
