  This work proposes a Bayesian rule based on the mixture of a point mass
function at zero and the logistic distribution to perform wavelet shrinkage in
nonparametric regression models with stationary errors (with short or
long-memory behavior). The proposal is assessed through Monte Carlo experiments
and illustrated with real data. Simulation studies indicate that the precision
of the estimates decreases as the amount of correlation increases. However,
given a sample size and error correlated noise, the performance of the rule is
almost the same while the signal-to-noise ratio decreases, compared to the
performance of the rule under independent and identically distributed errors.
Further, we find that the performance of the proposal is better than the
standard soft thresholding rule with universal policy in most of the considered
underlying functions, sample sizes and signal-to-noise ratios scenarios.
