论文标题
对成对正常分布的Matusita重叠系数的估计
Estimation of Matusita Overlapping Coefficient for Pair Normal Distributions
论文作者
论文摘要
MATUSITA重叠系数定义为两个或多个分布之间的一致性或相似性。参数正态分布是最重要的统计分布之一。假设手头的数据遵循两个独立的正常分布，本文提出了一种估计Matusita系数的新技术。与文献中的研究相反，建议的技术不需要对正常分布的位置和比例参数的假设。研究了所得估计器的有限性能，并与非参数内核估计器以及通过仿真技术与一些现有估计器进行了比较。结果表明，在所有考虑的情况下，提出的估计器的性能优于内核估计器。
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.
