Bayesian and likelihood methods for fitting multilevel models with complex level-1 variation.

DOI10.1016/S0167-9473(01)00058-5zbMath1132.62312OpenAlexW2113359871MaRDI QIDQ1605374

William J. Browne, Harvey Goldstein, Jon Rasbash, David Draper

Publication date: 15 July 2002

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-9473(01)00058-5

zbMATH Keywords

Markov chain Monte Carlo (MCMC)HeteroscedasticityIGLSRIGLSMCMC efficiencyAdaptive Metropolis-Hastings samplingEducational dataHierarchical modellingMaximum-likelihood methodsMultilevel modelling

Mathematics Subject Classification ID

Applications of statistics to social sciences (62P25) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (5)

Using retrospective sampling to estimate models of relationship status in large longitudinal social networks ⋮ Choice of units of analysis and modeling strategies in multilevel hierarchical models ⋮ MCMC algorithms for constrained variance matrices ⋮ Double Hierarchical Generalized Linear Models (With Discussion) ⋮ Analyzing the emergence times of permanent teeth: an example of modeling the covariance matrix with interval-censored data

Uses Software

• alr3
• MLwiN
• HLM
• BUGS
• BayesDA
• Gibbsit

Cites Work

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• Implementation and performance issues in the Bayesian and likelihood fitting of multilevel models
• Multilevel mixed linear model analysis using iterative generalized least squares
• Restricted unbiased iterative generalized least-squares estimation
• Model choice: a minimum posterior predictive loss approach
• Adaptive Rejection Sampling for Gibbs Sampling
• A comparison of Bayesian and likelihood-based methods for fitting multilevel models

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