The Effect Of Missing Data On Robust Bayesian Spectral Analysis
Jacqueline Christmas

We investigate the effects of missing observations on the
robust Bayesian model for spectral analysis introduced by
Christmas [2013]. The model assumes Student-t distributed
noise and uses an automatic relevance determination prior on
the precisions of the amplitudes of the component sinusoids
and it is not obvious what their effect will be when some of
the otherwise temporally uniformly sampled data is missing.