Parametric bootstrap methods for bias correction in linear mixed models

DOI10.1016/J.JMVA.2011.12.002zbMath1236.62018OpenAlexW1572035573MaRDI QIDQ765823

Bui Nagashima, Tatsuya Kubokawa

Publication date: 22 March 2012

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2011.12.002

zbMATH Keywords

confidence intervalsmean squared errormaximum likelihood estimatorsmall area estimationrestricted maximum likelihood estimatorFay-Herriot modelbest linear unbiased predictorsecond-order correctionnested error regression modelempirical Bayes procedure

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Parametric tolerance and confidence regions (62F25) Linear regression; mixed models (62J05) Bayesian inference (62F15) Bootstrap, jackknife and other resampling methods (62F40)

Related Items (7)

A resampling approach to estimation of the linking variance in the Fay–Herriot model ⋮ Mean-Squared errors of small area estimators under a multivariate linear model for repeated measures data ⋮ Small area estimation under a temporal bivariate area-level linear mixed model with independent time effects ⋮ Comparison of mean squared error estimators under the Fay-Herriot model: application to poverty and percentage of food expenditure data ⋮ Modified conditional AIC in linear mixed models ⋮ Small Area Estimation via Multivariate Fay-Herriot Models with Latent Spatial Dependence ⋮ Bootstrap estimation of uncertainty in prediction for generalized linear mixed models

Uses Software

• Ox
• saery

Cites Work

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