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.

翻译：半连续数据来自零点质量和连续分布的分布,同时支持正正正正正线。一个明显的例子就是每日降雨量数据。在本文中,我们提出了一个新奇的算法,用最大恒温原则估计半连续数据的密度函数。与文献中的现有方法不同,我们的算法仅需要引温最大化问题中制约函数的样本值,而不需要整个样本。我们用模拟来显示,我们算法产生的酶估计值与现有方法相比,与现有方法相比,偏差要小得多。提供了每日降雨数据的应用程序。