Journal of Data Science

Abstract: Constrained general linear models (CGLMs) have wide applications in practice. Similar to other data analysis, the identification of influential obser vations that may be potential outliers is an important step beyond in CGLMs. We develop local influence approach for detecting influential observations in CGLMs. The procedure makes use of the normal curvature and the direction achieving the maximum curvature to assess the local influences of minor perturbation of CGLMs. An illustrative example with a real data set is also reported.