Application of ‘Delete = Replace’ to Deletion Diagnostics for Variance Component Estimation in the Linear Mixed Model

DOI10.1046/j.1369-7412.2003.05211.xzbMath1060.62081OpenAlexW2167548031MaRDI QIDQ4665836

John Haslett, Dominic Dillane

Publication date: 11 April 2005

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1046/j.1369-7412.2003.05211.x

zbMATH Keywords

generalized least squaresleveragerestricted maximum likelihoodconditional residuals

Mathematics Subject Classification ID

Analysis of variance and covariance (ANOVA) (62J10) Diagnostics, and linear inference and regression (62J20)

Related Items

Influence diagnostics and outlier tests for varying coefficient mixed models ⋮ Case deletion diagnostics for GMM estimation ⋮ Local Influence Analysis in AB-BA Crossover Designs ⋮ Multiple Cases Deletion Diagnostics for Linear Mixed Models ⋮ Influence measures and outliers detection in linear mixed measurement error models with Ridge estimation ⋮ Deletion, replacement and mean-shift for diagnostics in linear mixed models ⋮ Residual analysis of linear mixed models using a simulation approach ⋮ Local influence in multilevel models ⋮ A variance shift model for detection of outliers in the linear mixed model ⋮ A variance shift model for detection of outliers in the linear measurement error model ⋮ Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics ⋮ Detection of outliers in multilevel models ⋮ Case deletion diagnostics in multilevel models ⋮ Estimating large-scale general linear and seemingly unrelated regressions models after deleting observations ⋮ DELETE-2 AND DELETE-3 JACKKNIFE PROCEDURES FOR UNMASKING IN REGRESSION ⋮ Deletion measures for generalized linear mixed effects models ⋮ A variance shift model for detection of outliers in the linear mixed measurement error models ⋮ Explicit influence analysis in two-treatment balanced crossover models

Uses Software

• R
• S-PLUS

Cites Work

• Checking the assumptions in mixed-model analysis of variance: a residual analysis approach
• Changes in the general linear model: A unified approach
• Linear mixed models in practice: a SAS-oriented approach
• Influence Analysis Based on the Case Sensitivity Function
• Residuals for the Linear Model With General Covariance Structure
• Some Algebra and Geometry for Hierarchical Models, Applied to Diagnostics
• A Simple Derivation of Deletion Diagnostic Results for the General Linear Model with Correlated Errors
• Simulation-Based Diagnostics in Random-Coefficient Models
• Informative Drop-Out in Longitudinal Data Analysis
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