标题： 对正态分布对的Matusita重叠系数的估计 显示英文标题

标题： Estimation of Matusita Overlapping Coefficient for Pair Normal Distributions

摘要： 马图西塔重叠系数定义为两个或多个分布之间的协议或相似性。 参数正态分布是最重要的统计分布之一。 在假设手头数据遵循两个独立正态分布的情况下，本文提出了一种新方法来估计马图西塔系数。 与文献中的研究不同，所建议的方法不需要对正态分布的位置和尺度参数做出任何假设。 通过模拟技术，研究了所得估计量的有限性质，并与非参数核估计量和一些现有估计量进行了比较。 结果表明，所提出的估计量在所有考虑的情况下表现优于核估计量。 显示英文摘要

摘要： The Matusita overlapping coefficient is defined as agreement or similarity between two or more distributions. The parametric normal distribution is one of the most important statistical distributions. Under the assumption that the data at hand follow two independent normal distributions, this paper suggests a new technique to estimate the Matusita coefficient. In contrast to the studies in the literature, the suggested technique requires no assumptions on the location and scale parameters of the normal distributions. The finite properties of the resulting estimators are investigated and compared with the nonparametric kernel estimators and with some existing estimators via simulation techniques. The results show that the performance of the proposed estimators is better than the kernel estimators for all considered cases.