Non parametric asymptotic distributions of Pianka’s and MacArthur-Levins measures

This article studies the asymptotic behaviors of non parametric 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.