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Titlebook: Robust and Nonlinear Time Series Analysis; Proceedings of a Wor Jürgen Franke,Wolfgang Härdle,Douglas Martin Conference proceedings 1984 Sp

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0930-0325 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 covarian
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Asymptotic Behaviour of the Estimates Based on Residual Autocovariances for ARMA Models,del. They show using a Monte Carlo study that this class contains estimates which are highly efficient when the observations correspond, to a perfectly observed Gaussian ARMA model and robust under the presence of outliers. In this paper we show the consistency and asymptotic normality of a class of
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