论文标题
Pianka和MacArthur-Levins措施的非参数渐近分布
Nonparametric Asymptotic Distributions of Pianka's and MacArthur-Levins Measures
论文作者
论文摘要
本文研究了两种重叠措施的非参数估计量的渐近行为，即皮安卡（Pianka）和麦克阿瑟·莱维斯（MacArthur-Levins）的措施。插入原理和内核密度估计方法用于估计此类措施。随机过程功能的限制理论用于研究这些估计值的限制行为。结果表明，在合适的假设下，这两个限制分布都是正常的。结果是在更一般的密度函数及其内核估计器的情况下获得的。这些条件适合处理各种应用。还进行了一项小型仿真研究以支持理论发现。最后，为说明目的分析了真实的数据集。
This article studies the asymptotic behaviors of nonparametric estimators of two overlapping measures, namely Pianka's and MacArthur-Levins measures. The plug-in principle and the method of kernel density estimation are used to estimate such measures. The limiting theory of the functional of stochastic processes is used to study limiting behaviors of these estimators. It is shown that both limiting distributions are normal under suitable assumptions. The results are obtained in more general conditions on density functions and their kernel estimators. These conditions are suitable to deal with various applications. A small simulation study is also conducted to support the theoretical findings. Finally, a real data set has been analyzed for illustrative purposes.
