ARMA spectral estimation of time series with missing observations

The problem of estimating the power spectral density of stationary time series when the measurements are not contiguous is considered. A new autoregressive moving-average (ARMA) method is proposed for this problem, based on nonlinear optimization of a weighted-squared-error criterion. The method can handle either regularly or randomly missing observations. As a special case, the method can handle the problem of missing sample covariances. The computational complexity is modest compared to exact maximum likelihood estimation of the same parameters. The performance of the algorithm is illustrated by some numerical examples and is shown to be statistically efficient in these cases.