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140122  eng 
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a 9781461578215

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a Franke, J.
e [editor]

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a Robust and Nonlinear Time Series Analysis
h Elektronische Ressource
b Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 “Stochastische Mathematische Modelle”, Heidelberg 1983
c edited by J. Franke, W. Härdle, D. Martin

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a 1st ed. 1984

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a New York, NY
b Springer New York
c 1984, 1984

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a 286 p
b online resource

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0 

a 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 ErrorinVariables Models  MinimaxRobust Filtering and FiniteLength 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  GrossError Sensitivies of GM and RAEstimates  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 SEstimators  On Robust Estimation of Parameters for Autoregressive Moving Average Models

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a Applied mathematics

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a Engineering mathematics

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a Applications of Mathematics

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a Probability Theory and Stochastic Processes

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a Probabilities

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1 

a Härdle, W.
e [editor]

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a Martin, D.
e [editor]

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7 
a eng
2 ISO 6392

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b SBA
a Springer Book Archives 2004

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a Lecture Notes in Statistics

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u https://doi.org/10.1007/9781461578215?nosfx=y
x Verlag
3 Volltext

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a 519.2

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a 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, this assumption is frequently replaced by the weaker assumption that the process is wide~sense stationary and that only the mean and covariance sequence is specified. This approach of specifying the probabilistic behavior only up to "second order" has of course been extremely popular from a theoretical point of view be cause it has allowed one to treat a large variety of problems, such as prediction, filtering and smoothing, using the geometry of Hilbert spaces. While the literature abounds with a variety of optimal estimation results based on either the Gaussian assumption or the specification of secondorder properties, time series workers have not always believed in the literal truth of either the Gaussian or secondorder specifica tion. They have nonetheless stressed the importance of such optimali ty results, probably for two main reasons: First, the results come from a rich and very workable theory. Second, the researchers often relied on a vague belief in a kind of continuity principle according to which the results of time series inference would change only a small amount if the actual model deviated only a small amount from the assum ed model
