  Signal processing of uniformly spaced data from stationary stochastic
processes with missing samples is investigated. Besides randomly and
independently occurring outliers also correlated data gaps are investigated.
Non-parametric estimators for the mean value, the signal variance, the
autocovariance and cross-covariance functions and the corresponding power
spectral densities are given, which are bias-free, independent of the spectral
composition of the data gaps. Bias-free estimation is obtained by averaging
over valid samples only from the data set. The procedures abstain from
interpolation of missing samples. An appropriate bias correction is used for
cases where the estimated mean value is subtracted out from the data. Spectral
estimates are obtained from covariance functions using Wiener-Khinchin's
theorem.
