摘要：We consider in the following the problem of robust spectral density estimation.
Unfortunately, conventional spectral density estimators are not robust in the presence
of additive outliers (cf. [18]). In order to get a robust estimate of the spectral
density function, it turned out that cleaning the time series in a robust way first
and calculating the spectral density function afterwards leads to encouraging results.
To meet these needs of cleaning the data we use a robust version of the Kalman filter
which was proposed by Ruckdeschel ([26]). Similar ideas were proposed by Martin
and Thomson ([18]).
Both methods were implemented in R (cf. [23]) and compared by extensive simulation
experiments. The competitive method is also applied to real data. As a special practical
application we focus on actual heart rate variability measurements of diabetes
patients.
关键词：robustness; spectral density function; AO-model
