  Semi-continuous data comes from a distribution that is a mixture of the point
mass at zero and a continuous distribution with support on the positive real
line. A clear example is the daily rainfall data. In this paper, we present a
novel algorithm to estimate the density function for semi-continuous data using
the principle of maximum entropy. Unlike existing methods in the literature,
our algorithm needs only the sample values of the constraint functions in the
entropy maximization problem and does not need the entire sample. Using
simulations, we show that the estimate of the entropy produced by our algorithm
has significantly less bias compared to existing methods. An application to the
daily rainfall data is provided.
