Asymptotic Theory of Statistical Inference for Time Series

This book PDF is perfect for those who love Mathematics genre, written by Masanobu Taniguchi and published by Springer Science & Business Media which was released on 06 December 2012 with total hardcover pages 671. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Asymptotic Theory of Statistical Inference for Time Series books below.

Asymptotic Theory of Statistical Inference for Time Series
Asymptotic Theory of Statistical Inference for Time Series
Author : Masanobu Taniguchi
File Size : 55,9 Mb
Publisher : Springer Science & Business Media
Language : English
Release Date : 06 December 2012
ISBN : 9781461211624
Pages : 671 pages
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Asymptotic Theory of Statistical Inference for Time Series by Masanobu Taniguchi Book PDF Summary

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.

Asymptotic Theory of Statistical Inference for Time Series

Asymptotic Theory of Statistical Inference for Time Series by Masanobu Taniguchi,Yoshihide Kakizawa

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

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Research Papers in Statistical Inference for Time Series and Related Models

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This book compiles theoretical developments on statistical inference for time series and related models in honor of Masanobu Taniguchi's 70th birthday. It covers models such as long-range dependence models, nonlinear conditionally heteroscedastic time series, locally stationary processes, integer-valued time series, Lévy Processes, complex-valued time series, categorical time series, exclusive

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