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Asymptotic Theory of Statistical Inference for Time Series || by Masanobu Taniguchi, Yoshihide Kakizawa (auth.) is a nonfiction available to read on EtoBox.

What is Asymptotic Theory of Statistical Inference for Time Series || about?

There has been much demand for the statistical analysis of dependent ob­ servations in many fields, for example, economics, engineering and the nat­ ural sciences. A model that describes the probability structure of a se­ ries of dependent observations is called a stochastic process. 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 autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) processes. We deal with a wide variety of stochastic processes, for example, non-Gaussian linear processes, long-memory processes, nonlinear processes, orthogonal increment process­ es, and continuous time processes. For them we develop not only the usual estimation and testing theory but also many other statistical methods and techniques, such as discriminant analysis, cluster analysis, nonparametric methods, higher order asymptotic theory in view of differential geometry, large deviation principle, and saddlepoint approximation. Because it is d­ ifficult to use the exact distribution theory, the discussion is based on the asymptotic theory.

Who reads Asymptotic Theory of Statistical Inference for Time Series ||?

Author

Masanobu Taniguchi, Yoshihide Kakizawa (auth.)

Publisher

Springer London, Limited

Published

2000

Language

EN

ISBN

9781461270287

Category

nonfiction

Subjects

Mathematics, Stem