Robust and Nonlinear Time Series Analysis Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 “Stochastische Mathematische Modelle”, Heidelberg 1983
Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. The, most commonly used assumption is that the data is a realization of a stationary Gaussian process. However, since the Gaussian assumption is a fairly stringent one, t...
| Other Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Springer New York
1984, 1984
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| Edition: | 1st ed. 1984 |
| Series: | Lecture Notes in Statistics
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- On the Use of Bayesian Models in Time Series Analysis
- Order Determination for Processes with Infinite Variance
- Asymptotic Behaviour of the Estimates Based on Residual Autocovariances for ARMA Models
- Parameter Estimation of Stationary Processes with Spectra Containing Strong Peaks
- Linear Error-in-Variables Models
- Minimax-Robust Filtering and Finite-Length Robust Predictors
- The Problem of Unsuspected Serial Correlations
- The Estimation of ARMA Processes
- How to Determine the Bandwidth of some Nonlinear Smoothers in Practice
- Remarks on NonGaussian Linear Processes with Additive Gaussian Noise
- Gross-Error Sensitivies of GM and RA-Estimates
- Some Aspects of Qualitative Robustness in Time Series
- Tightness of the Sequence of Empiric C.D.F. Processes Defined from Regression Fractiles
- Robust Nonparametric Autoregression
- Robust Regression by Means of S-Estimators
- On Robust Estimation of Parameters for Autoregressive Moving Average Models