Athens Conference on Applied Probability and Time Series Analysis: Volume II: Time Series Analysis In Memory of E.J. HannanThe Athens Conference on Applied Probability and Time Series in 1995 brought together researchers from across the world. The published papers appear in two volumes. Volume II presents papers on time series analysis, many of which were contributed to a meeting in March 1995 partly in honour of E.J. Hannan. The initial paper by P.M. Robinson discusses Ted Hannan's researches and their influence on current work in time series analysis. Other papers discuss methods for finite parameter Gaussian models, time series with infinite variance or stable marginal distribution, frequency domain methods, long range dependent processes, nonstationary processes, and nonlinear time series. The methods presented can be applied in a number of fields such as statistics, applied mathematics, engineering, economics and ecology. The papers include many of the topics of current interest in time series analysis and will be of interest to a wide range of researchers. |
Contents
Preface | 1 |
A Note on Chaotic Maps and Time Series | 15 |
A Structure Theory for Identification | 27 |
Copyright | |
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approximation ARMA process assume assumption asymptotic theory autocorrelation autocovariances autoregressive bandwidth bias Biometrika Brockwell central limit theorem coefficients component computed consider convergence correlation corresponding covariance defined Deistler denote distribution efficiency equation Figure filters finite Fourier frequency Gaussian given independent infinite variance integrated iterative kernel least squares Lemma limit theorem linear long-memory long-range dependence M-estimator Markov matrix maximum likelihood mean squared error method moving average noise nonlinear nonparametric normal observations obtained optimal bandwidth outliers parameter periodogram polynomial probability problem procedure Proof properties random variables regression residuals Robinson robust sample satisfies Section sequence Series Analysis series models simulation smoothness spectral density spectrum stable Paretian stationary process Statist stochastic process Taqqu transfer function trend trend estimation unit root values vector volatility wavelet Whittle X₁ Y₁ zero