  One key issue in several astrophysical problems is the evaluation of the
density probability function underlying an observational discrete data set. We
here review two non-parametric density estimators which recently appeared in
the astrophysical literature, namely the adaptive kernel density estimator and
the Maximum Penalized Likelihood technique, and describe another method based
on the wavelet transform.
  The efficiency of these estimators is tested by using extensive numerical
simulations in the one-dimensional case. The results are in good agreement with
theoretical functions and the three methods appear to yield consistent
estimates.
  In order to check these estimators with respect to previous studies, two
galaxy redshift samples (the galaxy cluster A3526 and the Corona Borealis
region) have been analyzed.
