This is the first book that integrates useful parametric and nonparametric techniques with time series modeling and prediction, the two important goals of time series analysis. Such a book will benefit researchers and practitioners in various fields such as econometricians, meteorologists, biologists, among others who wish to learn useful time series methods within a short period of time. The book also intends to serve as a reference or text book for graduate students in statistics and econometrics.
Nonlinear Time Series
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Editorial Reviews
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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
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Most Helpful Customer Reviews
4 of 4 people found the following review helpful:
5.0 out of 5 stars
Well used already!,
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This review is from: Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics) (Paperback)
This is an excellent monograph. The authors have provided an up-to-date analysis of parametric and nonparametric methods with a comprehensive bibliography. The book is very readible. The authors combine elements of descriptive overview, nontrivial examples and theorems / proofs.
3 of 3 people found the following review helpful:
5.0 out of 5 stars
A good book,
This review is from: Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics) (Paperback)
This book introduces some basal concepts and also includes the recent technology in nonlinear time series. It's not difficult to understand even if you are a novel. The cited references may help you to go further steps in this area.
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Inside This Book
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First Sentence:
In attempts to understand the world around us, observations are frequently made sequentially over time. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
strictly stationary solution, lynx data, modeling biases, estimated coefficient functions, local data points, nonlinear time series models, local linear fit, local linear estimator, bilinear time series models, quadratic approximation lemma, local polynomial estimator, generalized likelihood ratio statistic, local polynomial fitting, backfitting algorithm, quantile interval, predictive sets, estimated spectral density, analyzing time series data, predictive intervals, bootstrap scheme, spectral density estimation, conditional variance function, strictly stationary process, local linear regression, local polynomial regression Key Phrases - Capitalized Phrases (CAPs): (learn more)
Lag Lag, Estimated Coefficient Function, Monte Carlo, Mackenzie River, Nadaraya Watson, Prakasa Rao, Features of Nonlinear Prediction, Royal Statistical Society New!
Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
In attempts to understand the world around us, observations are frequently made sequentially over time. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
strictly stationary solution, lynx data, modeling biases, estimated coefficient functions, local data points, nonlinear time series models, local linear fit, local linear estimator, bilinear time series models, quadratic approximation lemma, local polynomial estimator, generalized likelihood ratio statistic, local polynomial fitting, backfitting algorithm, quantile interval, predictive sets, estimated spectral density, analyzing time series data, predictive intervals, bootstrap scheme, spectral density estimation, conditional variance function, strictly stationary process, local linear regression, local polynomial regression Key Phrases - Capitalized Phrases (CAPs): (learn more)
Lag Lag, Estimated Coefficient Function, Monte Carlo, Mackenzie River, Nadaraya Watson, Prakasa Rao, Features of Nonlinear Prediction, Royal Statistical Society New!
Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
Citations (learn more)
This book cites 100
books:
See all 100 books this book cites
36
books
cite this book:
See all 36 books citing this book
- Chaos: A Statistical Perspective (Springer Series in Statistics) by Kung-Sik Chan on 4 pages
- Multivariate Analysis: 1st: International Symposium Proceedings by Paruchuri R. Krishnaiah in Back Matter (1), and Back Matter (2)
- Time Series Analysis: Forecasting & Control (3rd Edition) by George Box in Back Matter (1), and Back Matter (2)
- Nonlinear Time Series by Jianqing Fan in Front Matter (1), and Front Matter (2)
- Time Series Analysis (Science Paperbacks) by E. J. Hannan in Back Matter (1), and Back Matter (2)
See all 100 books this book cites
- Inference in Hidden Markov Models (Springer Series in Statistics) by Olivier Cappé in Front Matter
- Introduction to Variance Estimation (Springer Series in Statistics) by Kirk Wolter in Front Matter
- Modern Econometric Analysis: Surveys on Recent Developments by Olaf Hübler on page 154
- Statistical Analysis of Financial Data in S-PLUS by Rene A. Carmona in Back Matter
- Resampling Methods for Dependent Data (Springer Series in Statistics) by S. N. Lahiri in Front Matter
See all 36 books citing this book
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