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As we can see, when a series generated by the EGARCH model is contaminated with one single large negative outlier, we may wrongly conclude that there is not leverage effect since all the cross-correlations become nearly zero.
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When the data used to fit an heteroscedastic nonparametric regression model are contaminated with outliers, robust estimators of the scale function are needed in order to obtain robust estimators of the regression function and to construct robust confidence bands.
When the data used to fit a nonparametric regression model are contaminated with outliers, we need to use a robust estimator of scale in order to make robust estimation of the regression function possible.
This initially led us to think that our model was contaminated by some diamagnetic effect.
The results are obtained when response samples from analytical model are contaminated by 5%% noise.
For simplicity and because we are fitting the models to a single model output only, which is contaminated with noise, we let K i =.
In practice, the data is contaminated by noise, which we represent by the following data model (5).
Nothing is contaminated.
"The water is contaminated, very contaminated.
"Berlin is contaminated.
Water is contaminated.
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