Comparison of naïve, Kenward–Roger, and parametric bootstrap interval approaches to small-sample inference in linear mixed models

DOI10.1080/03610918.2015.1019002zbMath1368.62077OpenAlexW2302325669MaRDI QIDQ5267887

Vincent S. Staggs

Publication date: 13 June 2017

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2015.1019002

zbMATH Keywords

hierarchical modelmixed modelparametric bootstrapmultilevel modelmean square error (MSE)

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Bootstrap, jackknife and other resampling methods (62F40)

Related Items (2)

Use of between-within degrees of freedom as an alternative to the Kenward–Roger method for small-sample inference in generalized linear mixed modeling of clustered count data ⋮ An adaptation of pseudo-score confidence interval method for linear mixed models

Uses Software

• SAS
• SAS/STAT

Cites Work

• Unnamed Item
• Random-Effects Models for Longitudinal Data
• An improved approximation to the precision of fixed effects from restricted maximum likelihood
• Bootstrap methods: another look at the jackknife
• On measures of uncertainty of empirical Bayes small-area estimators
• A unified jackknife theory for empirical best prediction with \(M\)-estimation
• Synthesis of variance
• On Parametric Bootstrap Methods for Small Area Prediction
• Bootstrap mean squared error of a small-area EBLUP
• Assessing different uncertainty measures of EBLUP: a resampling-based approach
• Two-Stage Analysis Based on a Mixed Model: Large-Sample Asymptotic Theory and Small-Sample Simulation Results
• Unbiasedness of two-stage estimation and prediction procedures for mixed linear models
• Nonparametric standard errors and confidence intervals
• Small Sample Inference for Fixed Effects from Restricted Maximum Likelihood
• The Mean Squared Error of Small Area Predictors Constructed With Estimated Area Variances
• IV.—On Least Squares and Linear Combination of Observations
• Mixed Models
• Bootstrap Methods
• Performance of the Kenward–Roger Method when the Covariance Structure is Selected Using AIC and BIC
• Thermodynamics of two-phase-systems
• The bootstrap and Edgeworth expansion

This page was built for publication: Comparison of naïve, Kenward–Roger, and parametric bootstrap interval approaches to small-sample inference in linear mixed models