An influential observation is any point that has a huge effect on the coefficients of a regression line fitting the data. The presence of such observations in the data set reduces the sensitivity and validity of the statistical analysis. In the literature there are many methods used for identifying influential observations. However, many of those methods are highly influenced by masking and swamping effects and require distributional assumptions. Especially in the presence of influential subsets most of these methods are insufficient to detect these observations. This study aims to develop a new diagnostic tool for identifying influential observations using the meta-heuristic binary particle swarm optimization algorithm. This proposed approach does not require any distributional assumptions and also not affected by masking and swamping effects as the known methods. The performance of the proposed method is analyzed via simulations and real data set applications.

有影响的观察是对拟合数据的回归线的系数有巨大影响的任何点。数据集中此类观察的存在降低了统计分析的敏感性和有效性。在文献中，有许多方法用于识别有影响的观察结果。然而，其中许多方法受到掩蔽和淹没效应的高度影响，并且需要分布假设。特别是在存在有影响的子集的情况下，这些方法中的大多数都不足以检测到这些观察结果。本研究旨在开发一种新的诊断工具，用于使用元启发式二元粒子群优化算法识别有影响的观察结果。这种提议的方法不需要任何分布假设，也不受已知方法的掩蔽和淹没效应的影响。通过仿真和实际数据集应用分析了所提出方法的性能。