Bayesian model selection for multilevel models using integrated likelihoods.
Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the standard approach is a comparison of models u...
Main Authors: | Tom Edinburgh, Ari Ercole, Stephen Eglen |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2023-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0280046 |
Similar Items
-
Bayesian model selection for multilevel models using integrated likelihoods
by: Tom Edinburgh, et al.
Published: (2023-01-01) -
Sepsis-3 criteria in AmsterdamUMCdb: open-source code implementation
by: Tom Edinburgh, et al.
Published: (2022-03-01) -
Statistical inference : an integrated Bayesian/likelihood approach /
by: 482580 Aitkin, Murray A.
Published: (c201) -
Bayesian synthetic likelihood for stochastic models with applications in mathematical finance
by: Ramona Maraia, et al.
Published: (2023-06-01) - Bayesian and likelihood methods in statistics and econometrics /