Detecting Influential Observations Using Liu Estimator in Linear Mixed Measurement Error Models

In this paper, we focused on identifying influential observations using Liu corrected likelihood estimator (LCLE) in linear mixed measurement error models when multicollinearity is present. Based on LCLE, the residuals were analyzed for evaluating the validity of the assumptions of a model. Also, diagnostic measures were developed to identify influential and high-leverage observations. We considered an extension of Cook’s distance to determine influential observations based on the case deletion model. Finally, a real example and also simulation studies were provided to illustrate the performance of the influence measures.