Asymptotic Theory of Statistical Inference for Time Series (Hardcover, 2000 ed.)

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 ARMA processes. A wide variety of stochastic processes, e.g., non-Gaussian linear processes, long-memory processes, nonlinear processes, non-ergodic processes and diffusion processes are described. The authors discuss the usual estimation and testing theory and also many other statistical methods and techniques, e.g., discriminant analysis, nonparametric methods, semiparametric approaches, higher order asymptotic theory in view of differential geometry, large deviation principle and saddlepoint approximation. Because it is difficult to use the exact distribution theory, the discussion is based on the asymptotic theory. The optimality of various procedures is often shown by use of the local asymptotic normality (LAN) which is due to Le Cam. The LAN gives a unified view for the time series asymptotic theory.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Springer Series in Statistics
Release date:	August 2000
First published:	2000
Authors:	Masanobu Taniguchi • Yoshihide Kakizawa
Dimensions:	235 x 155 x 42mm (L x W x T)
Format:	Hardcover
Pages:	662
Edition:	2000 ed.
ISBN-13:	978-0-387-95039-6
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics
LSN:	0-387-95039-7
Barcode:	9780387950396

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