Nonparametric Asymptotic Distributions of Pianka's and MacArthur-Levins Measures

TL;DR

This paper investigates the asymptotic distributions of nonparametric estimators for Pianka's and MacArthur-Levins measures, demonstrating their normality under broad conditions and supporting findings with simulations and real data analysis.

Contribution

It provides a comprehensive theoretical framework for the asymptotic behavior of nonparametric estimators of two ecological overlap measures under general conditions.

Findings

Both estimators have normal limiting distributions under suitable assumptions.

The results hold for a wide class of density functions and kernel estimators.

Simulation and real data analysis support the theoretical results.

Abstract

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.