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In the statistical analysis of data, a model might be awfully fitted with the presence of outliers. Besides, it has been well established to use residuals for identification of outliers. The asymptotic properties of residuals can be utilized to contribute diagnostic tools. However, it is now evident that most of the existing diagnostic methods have failed in identifying multiple outliers. Therefore, this paper proposed a diagnostic method for the identification of multiple outliers in GLM, where traditionally used outlier detection methods are effortless as they undergo masking or swamping dilemma. Hence, an investigation was carried out to determine the capability of the proposed GSCPR method. The findings obtained from the numerical examples indicated that the performance of the proposed method was satisfactory for the identification of multiple outliers. Meanwhile, in the simulation study, two scenarios were considered to assess the validity of the proposed method. The proposed method consistently displayed higher percentage of correct detection, as well as lower rates of swamping and masking, regardless of the sample size and the contamination levels

Identification of Multiple Outliers in a Generalized Linear Model with Continuous Variables