Nonlinear Time Series

Nonparametric and Parametric Methods

Jianqing Fan, Qiwei Yao - Collection Springer Series In Statistics

552 pages, parution le 06/12/2005

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Résumé

This book presents the contemporary statistical methods and theory of nonlinear time series analysis. The principal focus is on nonparametric and semiparametric techniques developed in the last decade. It covers the techniques for modelling in state-space, in frequency-domain as well as in time-domain. To reflect the integration of parametric and nonparametric methods in analyzing time series data, the book also presents an up-to-date exposure of some parametric nonlinear models, including ARCH/GARCH models and threshold models. A compact view on linear ARMA models is also provided. Data arising in real applications are used throughout to show how nonparametric approaches may help to reveal local structure in high-dimensional data. Important technical tools are also introduced.

The book will be useful for graduate students, application-oriented time series analysts, and new and experienced researchers. It will have the value both within the statistical community and across a broad spectrum of other fields such as econometrics, empirical finance, population biology and ecology. The prerequisites are basic courses in probability and statistics.

L'auteur - Jianqing Fan

Jianqing Fan, coauthor of the highly regarded book Local Polynomial Modeling, is Professor of Statistics at the University of North Carolina at Chapel Hill and the Chinese University of Hong Kong. His published work on nonparametric modeling, nonlinear time series, financial econometrics, analysis of longitudinal data, model selection, wavelets and other aspects of methodological and theoretical statistics has been recognized with the Presidents' Award from the Committee of Presidents of Statistical Societies, the Hettleman Prize for Artistic and Scholarly Achievement from the University of North Carolina, and by his election as a fellow of the American Statistical Association and the Institute of Mathematical Statistics.

L'auteur - Qiwei Yao

Qiwei Yao is Professor of Statistics at the London School of Economics and Political Science. He is an elected member of the International Statistical Institute, and has served on the editorial boards for the Journal of the Royal Statistical Society (Series B) and the Australian and New Zealand Journal of Statistics.

Sommaire

Preface
Introduction
Characteristics of Time Series
ARMA Modeling and Forecasting
Parametric Nonlinear Time Series Models
Nonparametric Density Estimation
Smoothing in Time Series
Spectral Density Estimation and Its Applications
Nonparametric Models
Model Validation
Nonlinear Prediction
References
Author Index
Subject Index

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Jianqing Fan, Qiwei Yao
Collection	Springer Series In Statistics
Parution	06/12/2005
Nb. de pages	552
Format	15,5 x 23,5
Couverture	Broché
Poids	809g
Intérieur	Noir et Blanc
EAN13	9780387261423
ISBN13	978-0-387-26142-3