Estimation of time series spectra with randomly missing data

  title={Estimation of time series spectra with randomly missing data},
  author={P. Broersen and Robert Bos},
  journal={Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510)},
  pages={1718-1723 Vol.3}
Maximum likelihood theory presents an elegant asymptomatic solution for the estimation of the parameters of time series models. Unfortunately, the performance of algorithms is often disappointing in finite samples with missing data. The likelihood function for the estimated zeros of time series models is symmetric with respect to the unit circle. As a consequence, the unit circle is either a local maximum or a local minimum in the likelihood of moving average (MA) models. This is a trap for non… CONTINUE READING


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