Bootstrapped inference for variance parameters, measures of heterogeneity and random effects in multilevel logistic regression models

DOI10.1080/00949655.2020.1797738OpenAlexW3048472775MaRDI QIDQ5033467

George Leckie, Peter C. Austin

Publication date: 23 February 2022

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2020.1797738

zbMATH Keywords

bootstrapMonte Carlo simulationsvariancehierarchical modelmultilevel modelrandom-effects model

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• bootstrap
• bootlib

Cites Work

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• A Novel Bootstrap Procedure for Assessing the Relationship between Class Size and Achievement
• Variable selection for generalized linear mixed models by \(L_1\)-penalized estimation
• The effect of misspecifying the random-effects distribution in linear mixed models for longitudinal data
• Statistical Methods for Profiling Providers of Medical Care: Issues and Applications
• Interpreting Parameters in the Logistic Regression Model with Random Effects
• The effect of number of clusters and cluster size on statistical power and Type I error rates when testing random effects variance components in multilevel linear and logistic regression models
• Bootstrap tests for variance components in generalized linear mixed models
• Handbook of Multilevel Analysis
• Bias in Penalized Quasi-Likelihood Estimation in Random Effects Logistic Regression Models When the Random Effects Are not Normally Distributed
• Moving to a World Beyond “p < 0.05”

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