In self-reported data usually a phenomenon called `heaping' occurs, i.e.
survey participants round the values of their income, weight or height to some
degree. Additionally, respondents may be more prone to round off or up due to
social desirability. By ignoring the heaping process a severe bias in terms of
spikes and bumps is introduced when applying kernel density methods naively to
the rounded data. A generalized Stochastic Expectation Maximization (SEM)
approach accounting for heaping with potentially asymmetric rounding behaviour
in univariate kernel density estimation is presented in this work. The
introduced methods are applied to survey data of the German Socio-Economic
Panel and exhibit very good performance simulations.