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.

翻译：Matusita重叠系数的定义是两个或两个以上分布之间的协议或相似性。参数正常分布是最重要的统计分布之一。根据手头数据遵循两个独立的正常分布的假设,本文件建议采用新的技术来估计Matusita系数。与文献的研究不同,建议采用的技术不需要对正常分布的位置和规模参数进行假设。对由此得出的测算员的有限性质进行了调查,并与非参数内核测量员和一些现有的通过模拟技术进行的估测员进行了比较。结果显示,拟议的测算员的表现优于所有审议案件的内核测算员。