A Novel Collinearity-Influential Observation Diagnostic Measure Based on a Group Deletion Approach

摘要

High leverage points can induce or disrupt nuilticollinearitv patterns in data. Observations responsible for this problem are general I v known as collinearityinfluential observations. A significant amount of published work on the identification of collinearity-infiuential observations exists; however, we show in this article thai all commonly used detection techniques display greatly reduced sensitivity in the presence of multiple high leverage collinearity-infiuential observations. We propose a new measure based on a diagnostic robust group deletion approach. Some practical cutoff points for existing and developed diagnostics measures are also introduced. Numerical examples and simulation results show that the proposed measure provides significant improvement over the existing measures.