8 edition of Asymptotic Theory of Statistical Inference for Time Series (Springer Series in Statistics) found in the catalog.
August 11, 2000
Written in English
|The Physical Object|
|Number of Pages||661|
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Search this site: Humanities. Architecture and Environmental Design; Art History. 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 /5(15).
With this in perspective, this book presents a broad view of exact statistical inference and the development of asymptotic statistical inference, providing a justification for the use of. The topics discussed include: Asymptotic likelihood theory Quasi-likelihood Likelihood and efficiency Inference for counting processes Inference for semimartingale regression models The author addresses a number of stochastic modeling applications from engineering, economic systems, financial economics, and medical sciences.
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Asymptotic Theory of Statistical Inference for Time Series. Authors (view affiliations) Masanobu Taniguchi The primary aim of this book is to provide modern statistical techniques and theory for stochastic processes.
such as discriminant analysis, cluster analysis, nonparametric methods, higher order asymptotic theory in view of. : Asymptotic Theory of Statistical Inference for Time Series (Springer Series in Statistics) (): Taniguchi, Masanobu, Kakizawa, Yoshihide: BooksFormat: Hardcover.
Asymptotic Theory of Statistical Inference for Time Series. Authors: Taniguchi, Masanobu, Kakizawa, Yoshihide which is due to LeCam. This book is suitable as a professional reference book on statistical anal ysis of stochastic processes or as a textbook for students who specialize in statistics.
It will also be useful to researchers. Problems.- 4 Higher Order Asymptotic Theory for Stochastic Processes.- Introduction to Higher Order Asymptotic Theory.- Valid Asymptotic Expansions.- Higher Order Asymptotic Estimation Theory for Discrete Time Processes in View of Statistical Differential Geometry.- Higher Order Asymptotic Theory for Continuous Time Processes.
Get this from a library. Asymptotic theory of statistical inference for time series. [Masanobu Taniguchi; Yoshihide Kakizawa] -- "The primary aims of this book are to provide modern statistical techniques and theory for stochastic processes.
The stochastic processes mentioned here are not restricted to the usual AR, MA and. An up-to-date and concise description of recent results in probability theory and stochastic processes useful in the study of asymptotic theory of statistical inference.
Brings together new material on the interplay between recent advances in probability theory and their applications to the asymptotic theory of statistical by: Asymptotic Theory of Statistical Inference for Time Series (Springer Series in Statistics) Masanobu Taniguchi, Yoshihide Kakizawa The primary aim of this book is to provide modern statistical techniques and theory for stochastic processes.
Asymptotic Theory of Statistical Inference for Time Series. Journal of the American Statistical Association: Vol. 97, No.pp. Author: Valentina Corradi. In such cases, we can still however base the inference on large-sample approximations to the distribution of the statistic in question. The mathematical tools for deriving such approximations are developed in this chapter.
A comprehensive treatment of asymptotic theory is given in the book of Serfling (). Statistical inference is the process of using data analysis to deduce properties of an underlying distribution of probability. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving is assumed that the observed data set is sampled from a larger population.
Inferential statistics can be contrasted with descriptive. In a problem of statistical inference if we use a statistic T n based on a random multivariate analysis, time series, and resampling.
The book presents subjects such as "maximum likelihood and sufficiency," and is written with an intuitive, heuristic approach to build reader comprehension. Theory and Methods of Statistics covers. Principles of Statistical Inference In this important book, D.
Cox develops the key concepts of the theory of statistical inference, in particular describing and comparing the main ideas and controversies over foundational issues that have rumbled on for more than years.
Continuing a year career of contribution to statistical thought. Asymptotic Theory of Statistical Inference for Time Series by Masanobu Taniguchi Free Delivery Worldwide: Asymptotic Theory of Statistical Inference for Time Series: Hardback: Springer-Verlag New York Inc.: 01 Sep The primary aim of this book is to provide modern statistical techniques and theor More info.
The book brings together new material on the interplay between recent advances in probability theory and their applications to the asymptotic theory of statistical inference. Asymptotic theory of maximum likelihood and Bayes estimation, asymptotic properties of least squares estimators in non-linear regression, and estimators of parameters for.
In asymptotic theory, besides his contributions to bootstrap and high-dimensional statistical inference, in this paper I shall focus on four of his seminal papers on asymptotic expansions and. (ebook) Asymptotic Theory of Statistical Inference for Time Series () from Dymocks online store.
There has been much demand for the statistical analysis of. Australia’s leading bookseller for years. Download Statistical Inference In Science Springer Series In Statistics ebook PDF or Read Online books in PDF, Asymptotic Theory Of Statistical Inference For Time Series.
Author: Masanobu Taniguchi ISBN: including functional time series and spatially indexed functions. Specific inferential problems studied include two. New York: Springer), and to illustrate how the asymptotic inference problems associated with a wide variety of time series regression models fit into such a structural framework.
The models illustrated include many linear time series models, including cointegrated models and autoregressive models with unit roots that are of wide current by: Time series A time series is a series of observations x t, observed over a period of time. Typically the observations can be over an entire interval, randomly sampled on an interval or at xed time points.
Di erent types of time sampling require di erent approaches to the data analysis. This chapter discusses the asymptotic theory of Bayes solutions in estimation and testing when the observations are from a discrete parameter stochastic process.
It presents the fundamental theorem in the asymptotic theory of Bayesian inference, namely, the approach of the posterior density to the normal for discrete parameter stochastic processes. This is a different book on the asymptotic theory and its use in probability and statistical inference.
It covers a wide range of divergent topics where the large sample theory is useful and can be naturally applied. /5(2).Sprott: Statistical Inference in Science Stein: Interpolation of Spatial Data: Some Theory for Kriging Taniguchi/Kakizawa: Asymptotic Theory for Statistical Inference for Time Series butions and Likelihood Functions, 3rd edition Tillé: Sampling Algorithms Tsaitis: Semiparametric Theory and Missing Data.the asymptotic inference problems associated with a wide variety of time series regression models fit into such a structural framework.
The models illustrated include many linear time series models, including cointegrated models and auto-regressive models with unit roots that are of wide current interest. The general.