  The present work describes simulation studies to compare the performances of
bayesian wavelet shrinkage methods in estimating component curves from
aggregated functional data. To do so, five methods were considered: the
bayesian shrinkage rule under logistic prior by Sousa (2020), bayesian
shrinkage rule under beta prior by Sousa et al. (2020), Large Posterior Mode
method by Cutillo et al. (2008), Amplitude-scale invariant Bayes Estimator by
Figueiredo and Nowak (2001) and Bayesian Adaptive Multiresolution Smoother by
Vidakovic and Ruggeri (2001). Further, the so called Donoho-Johnstone test
functions, Logit and SpaHet functions were considered as component functions.
It was observed that the signal to noise ratio of the data had impact on the
performances of the methods.
