## Developments in Time Series AnalysisThis volume contains 27 papers, written by time series analysts, dealing with statistical theory, methodology and applications. The emphasis is on the recent developments in the analysis of linear, onlinear (non-Gaussian), stationary and nonstationary time series. The topics include cointegration, estimation and asymptotic theory, Kalman filtering, nonparametric statistical inference, long memory models, nonlinear models, spectral analysis of stationary and nonstationary processes. Quite a number of papers are devoted to modelling and analysis of real time series, and the econometricians, mathematical statisticians, communications engineers and scientists who use time series techniques and Fourier analysis should find the papers in this volume useful. |

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### Contents

Developments in multivariate covariance generation | 26 |

a review | 50 |

The Gaussian log likelihood and stationary sequences | 69 |

Exact maximum likelihood estimation for extended | 110 |

Determining the number of jumps in a spectrum | 127 |

Periodogram analysis for complexvalued time series | 149 |

A spectral approach to long memory time series | 164 |

Nonparametric function estimation in noisy chaos | 183 |

NonGaussian characteristics of exponential autoregressive | 257 |

Bispectrum based checking of linear predictability for time series | 274 |

Maximum likelihood fitting of bilinear models to time series | 283 |

Conditional maximum likelihood estimates for INARl | 310 |

dispersion and modes | 331 |

The prediction of timefrequency spectra using | 355 |

Time variable and state dependent modelling of nonstationary | 374 |

Demodulation of phase modulated signals | 414 |

### Other editions - View all

Developments in Time Series Analysis: In honour of Maurice B. Priestley M. B. Priestley,T. Subba Rao No preview available - 2013 |

### Common terms and phrases

Akaike algorithm application approach approximation ARIMA ARMA model assume assumptions autocorrelation autocovariance autoregressive model Bayesian Bhansali bilinear Biometrika bispectrum coefficients components computed conditional consider constant correlation covariance matrix criterion defined denote dependent derived discussed equation equivalent example ExpAR model exponential exponential family Figure finite fitting follows forecasting frequency frequency modulated Gaussian given Hannan Harrison independent innovation Kalman filter lemma likelihood function linear model maximum likelihood estimates Melard method model selection moving average multivariate non-stationary nonlinear nonlinear time series nonparametric observations obtained order selection Ozaki parameter estimation Parzen periodogram polynomial prediction predictor problem procedure Proof properties random variables recursive estimation regression sample sequence series analysis series models signal simulation smoothed estimate spectral analysis spectral density spectrum stationary process stochastic Subba Rao term theorem theory values variance variation vector white noise zero mean