Influence diagnostics and outlier tests for varying coefficient mixed models

Abstract

a b s t r a c tIn this paper, we consider subset deletion diagnostics for fixed effects (coefficient functions), random effects and one variance component in varying coefficient mixed models (VCMMs). Some simple updated formulas are obtained, and based on which, Cook's distance, joint influence and conditional influence are also investigated. Besides, since mean shift outlier models (MSOMs) are also efficient to detect outliers, we establish an equivalence between deletion models and MSOMs, which is not only suitable for fixed effects but also for random effects, and test statistics for outliers are then constructed.As a byproduct, we obtain the nonparametric ''delete = replace'' identity. Our influence diagnostics methods are illustrated through a simulated example and a real data set.

Hospital Readmission in Persons With Stroke Following Postacute Inpatient Rehabilitation

“…Testing was two-sided using p < .05 for all models. Regression diagnostics indicated no signifi cant outliers or collinearity among continuous variables ( 25 ) . Analyses were performed using SAS version 9.2 (SAS Institute, Inc., Cary, NC) and HLM version 6.0 (Scientifi c Software International, Inc).…”

Local influence for elliptical partially varying-coefficient model

“…Fung et al (2002) studied influence diagnostics for normal semiparametric mixed models with longitudinal data. Li et al (2009) derived influence measures and outlier test for partially varying-coefficient mixed model. Case deletion does not directly reflect the impact of other perturbations in the model.…”

A Variance Shift Model for Detection of Outliers in the Linear Measurement Error Model

“…Zewotir and Galpin [11] provided routine diagnostic tools for fixed effects, random effects, and variance components, which are computationally inexpensive. Li et al [12] considered subset deletion diagnostics for fixed effects, random effects and one variance component in varying coefficient mixed models. Gumedze et al [13] extended the variance shift outlier model (VSOM) to the linear mixed model.…”