An efficient method of identification of influential observations in multiple linear regression and its application to real data

Abstract

Influential observations (IOs) are those observations which either alone or together with several other observations have detrimental effect on the computed values of various estimates. As such, it is very important to detect their presence. Several methods have been proposed to identify IOs that include the fast improvised influential distance (FIID). The FIID method has been shown to be more efficient than some existing methods. Nonetheless, the shortcoming of the FIID method is that, it is computationally not stable, still suffers from masking and swamping effects, time consuming issues and not using proper cut-off point. As a solution to this problem, a new robust version of influential distance method (RFIID) which is based on Reweighted Fast Consistent and High Breakdown (RFCH) estimator is proposed. The results of real data and Monte Carlo simulation study indicate that the RFIID able to correctly separate the IOs from the rest of data with the least computational running times, least swamping effect and no masking effect compared to the other methods in this study.