… The spectral window has unit area with the area concentrated near the origin. When estimating
a spectral density without missing observations, often positive definite estimates are used…

… Note that the regression estimator is robust to possible missing observations. Then we apply
the spectral density estimator to detrended data. In all the previously described experiments…

… presented a parametric spectral estimation technique for time series with missing data, based
… regularly or randomly missing data and, as a special case, missing sample covariances. …

… data, as the constructions for missing data situations outlined above are only valid for sta…
the problem of spectral estimation for a non-stationary process with missing observations. In our …

… In this paper, we consider the spectrum estimation of multi… waveform is subject to missing
observations. The specific FH … and accurate spectrum estimation with missing observations, …

… There exist many algorithms and procedures in the literature that provide alternatives to
standard Fourier analysis for spectral estimation in the presence of missing data. Nevertheless, …

… X(t) is observed and O when X(t) is missmg. A spectral estimate which may be calculated …
A necessary and sufficient condition on the structure of m(t) is given for the spectral estimate …

… is devising a spectral estimator that operates on a finite sample such that the estimate is
not … The form used in this work for the high resolution spectral estimator averaged over the …

… This type of process does not cause serious problems for spectral estimation because, from
a spectrum estimation viewpoint, outliers in the er are in a sense good outliers. Such outliers …

… was much better than the accuracy of other spectral estimates for randomly missing data. …
Records with missing observations are often found in many scientific and industrial …