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Nonlinear Time Series: Nonparametric and Parametric Methods
(Springer Series in Statistics) (Paperback)
by Jianqing Fan, Qiwei Yao
"In attempts to understand the world around us, observations are frequently made sequentially over time..."
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"…the authors should be congratulated for writing a coherent monograph on modern time series analysis with a focus on nonparametric approaches. I believe that this book will become a standard reference in this area and remain so for a long time. Graduate students in statistics, economics, and financial engineering should be happy to have a much-needed textbook on modern time series methods, which covers not only ARIMA models, but also the newer and more flexible nonlinear and nonparametric techniques."
Technometrics, February 2004
"This is a book that one can read as a beginner or as an expert. Although there are plenty of theorems, there are also plenty of numerical examples, with both real and simulated data, and lots of pictures and graphics (SPLUS-style). The topics are very fully explained and discussed, and there are many pointers to the literature for further study (with about six hundred references listed)."
ISI Short Book Reviews, Vol. 24/1, Apr. 2004
"Fan and Yao's book has a lot to offer. First, it is readable, even by those with limited knowledge of time-series analysis, as the authors spend time on all the basic concepts. Second, it is self-contained so you do not need other books to understand it. Third, it contains many examples and illustrations to explain the intuition behind the concepts. Fourth, it is up to date and has the latest cutting-edge methods to handle nonlinear time series." Quantitative Finance, 2004
Book Description
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. 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. 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.
[此贴子已经被作者于2006-10-20 17:12:29编辑过]
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