Document Type : original

Author

• Fatemeh Ghapani

Department of Mathematics and Statistics, Sho.C., Islamic Azad University

Abstract

This article focuses on diagnostic measures for identifying high-leverage points, and influential observations in linear mixed measurement error (LMME) models. It achieves by imposing the stochastic restrictions on the parameters and incorporating the ridge estimator to tackle the issue of multicollinearity. To this end, generalized leverage matrices are defined using the restricted ridge estimator (RRE) to identify high-leverage observations. Additionally, analogs of Cook’s distance and likelihood distance are proposed to determine influential observations through a case deletion approach. Simulation studies and real-life applications support the theoretical results. To the best of our knowledge, there has been no significant attention in the literature regarding diagnostics for leverage and influence measures concerning the outcomes of the RRE in LMME models. Hence, this paper evaluates the influence of observations by using leverage and influence measures to identify influential observations on the RRE’s of fixed effects and the prediction of random effects in LMME models

Keywords

• Case deletion model
• Cook’s distance
• Diagnostics, Leverage points, Ridge estimator

Main Subjects

• Statistical Computing