  The entropy is a measure of uncertainty that plays a central role in
information theory. When the distribution of the data is unknown, an estimate
of the entropy needs be obtained from the data sample itself. We propose a
semi-parametric estimate, based on a mixture model approximation of the
distribution of interest. The estimate can rely on any type of mixture, but we
focus on Gaussian mixture model to demonstrate its accuracy and versatility.
Performance of the proposed approach is assessed through a series of simulation
studies. We also illustrate its use on two real-life data examples.

Methodology;Computation