论文标题
通过收缩来估计组成估计
Composition Estimation via Shrinkage
论文作者
论文摘要
在本说明中，我们使用对名义离散域上的惩罚可能性密度估计来探讨一种简单的组成估计方法。在模拟中研究了诸如平滑参数选择和使用先验信息之类的实际问题，并尝试了理论分析。该方法已在一对R函数中实现，以供从业人员使用。
In this note, we explore a simple approach to composition estimation, using penalized likelihood density estimation on a nominal discrete domain. Practical issues such as smoothing parameter selection and the use of prior information are investigated in simulations, and a theoretical analysis is attempted. The method has been implemented in a pair of R functions for use by practitioners.
