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Signal processing of uniformly spaced data from stationary stochasticprocesses with missing samples is investigated. Besides randomly andindependently occurring outliers also correlated data gaps are investigated.Non-parametric estimators for the mean value, the signal variance, theautocovariance and cross-covariance functions and the corresponding powerspectral densities are given, which are bias-free, independent of the spectralcomposition of the data gaps. Bias-free estimation is obtained by averagingover valid samples only from the data set. The procedures abstain frominterpolation of missing samples. An appropriate bias correction is used forcases where the estimated mean value is subtracted out from the data. Spectralestimates are obtained from covariance functions using Wiener-Khinchin'stheorem.

Bias-Free Estimation of the Auto- and Cross-Covariance and the Corresponding Power Spectral Densities from Gappy Data