Pulsar timing observations are usually analysed with least-square-fitting
procedures under the assumption that the timing residuals are uncorrelated
(statistically "white"). Pulsar observers are well aware that this assumption
often breaks down and causes severe errors in estimating the parameters of the
timing model and their uncertainties. Ad hoc methods for minimizing these
errors have been developed, but we show that they are far from optimal.
Compensation for temporal correlation can be done optimally if the covariance
matrix of the residuals is known using a linear transformation that whitens
both the residuals and the timing model. We adopt a transformation based on the
Cholesky decomposition of the covariance matrix, but the transformation is not
unique. We show how to estimate the covariance matrix with sufficient accuracy
to optimize the pulsar timing analysis. We also show how to apply this
procedure to estimate the spectrum of any time series with a steep red
power-law spectrum, including those with irregular sampling and variable error
bars, which are otherwise very difficult to analyse.Comment: Accepted by MNRA