论文标题
使用熵最大化的半连续数据的密度估计
Density Estimation using Entropy Maximization for Semi-continuous Data
论文作者
论文摘要
半连续数据来自一个分布，该分布是零点质量的混合物，并且连续分布与正面真实线的支持。一个明显的例子是每日降雨数据。在本文中，我们提出了一种新型算法，以使用最大熵的原理来估计半连续数据的密度函数。与文献中的现有方法不同，我们的算法仅需要熵最大化问题中约束函数的样本值，并且不需要整个样本。使用模拟，我们表明，与现有方法相比，我们算法产生的熵的估计值明显较小。提供了每日降雨数据的应用程序。
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.
