  We present a new approach for statistical inference on noise properties of
CMB anisotropy data. We consider a Maximum Likelihood parametric estimator to
recover the full dependence structure of the noise process. We also consider a
semiparametric procedure which is only sensitive to the low frequency behavior
of the noise spectral density. Both approaches are statistically robust and
computationally convenient in the case of long memory noise, even under
nonstationary circumstances. We show that noise properties can be consistently
derived by such procedures without resorting to currently used iterative
noise-signal methods. More importantly, we show that optimal (GLS) CMB maps can
be obtained from the observed timestream with the only knowledge of the noise
memory parameter, the outcome of our estimators.
