Regression analysis is one of the most widely used statistical techniques. It is well known that the least squares estimates is sensitive to atypical and/or influential observations. Many methodologies were proposed to detect influential observations considering case deletion (global influence). On the other hand, Cook (J R Stat Soc Ser B 48(2):133–169, 1986) developed a general and powerful methodology to obtain a group of observations that might be jointly influential considering the local influence. However, these techniques may fail to detect masked influential observations. In this paper, we propose a methodology to detect masked influential observations in a local influence framework considering the forward search (Atkinson and Riani, Robust diagnostic regression analysis, Springer, New York, 2000). The usefulness of the proposed methodology is illustrated with data sets which were previously analyzed in the literature to detect outliers and/or influential observations. Masked influential observations were successfully identified in these studies. The proposed methodology may be used in any model where the local influence analysis (Cook 1986) is appropriate.

回归分析是应用最广泛的统计技术之一。众所周知，最小二乘估计对非典型和/或有影响的观察很敏感。考虑到案例删除（全局影响），提出了许多方法来检测有影响的观察结果。另一方面，库克 (JR Stat Soc Ser B 48(2):133–169, 1986) 开发了一种通用且强大的方法来获得一组可能具有共同影响的观察结果，考虑到当地的影响。然而，这些技术可能无法检测被掩盖的有影响的观察结果。在本文中，我们提出了一种在考虑前向搜索的情况下在局部影响框架中检测被掩盖的影响观察的方法（Atkinson 和 Riani，鲁棒诊断回归分析，Springer，纽约，2000 年）。所提出的方法的有用性通过以前在文献中分析过的数据集来说明，以检测异常值和/或有影响的观察结果。在这些研究中成功地确定了被掩盖的有影响的观察结果。建议的方法可用于任何适合进行局部影响分析（Cook 1986）的模型。