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Robust and Nonlinear Time Series Analysis

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Cover of 'Robust and Nonlinear Time Series Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 On the Use of Bayesian Models in Time Series Analysis
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    Chapter 2 Order Determination for Processes with Infinite Variance
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    Chapter 3 Asymptotic Behaviour of the Estimates Based on Residual Autocovariances for ARMA Models
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    Chapter 4 Parameter Estimation of Stationary Processes with Spectra Containing Strong Peaks
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    Chapter 5 Linear Error-in-Variables Models
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    Chapter 6 Minimax-Robust Filtering and Finite-Length Robust Predictors
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    Chapter 7 The Problem of Unsuspected Serial Correlations
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    Chapter 8 The Estimation of ARMA Processes
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    Chapter 9 How to determine the bandwidth of some nonlinear smoothers in practice
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    Chapter 10 Remarks on Nongaussian Linear Processes with Additive Gaussian Noise
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    Chapter 11 Gross-Error Sensitivies of GM and RA-Estimates
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    Chapter 12 Some Aspects of Qualitative Robustness in Time Series
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    Chapter 13 Tightness of the Sequence of Empiric C.D.F. Processes Defined from Regression Fractiles
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    Chapter 14 Robust Nonparametric Autoregression
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    Chapter 15 Robust Regression by Means of S-Estimators
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    Chapter 16 On Robust Estimation of Parameters for Autoregressive Moving Average Models
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Title
Robust and Nonlinear Time Series Analysis
Published by
Springer US, December 2012
DOI 10.1007/978-1-4615-7821-5
ISBNs
978-0-387-96102-6, 978-1-4615-7821-5
Editors

Franke, Jürgen, Härdle, Wolfgang, Martin, Douglas

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