Joint Selection in Mixed Models using Regularized PQL
The application of generalized linear mixed models presents some major challenges for both estimation, due to the intractable marginal likelihood, and model selection, as we usually want to jointly select over both fixed and random effects. We propose to overcome these challenges by combining penalized quasi-likelihood (PQL) estimation with sparsity inducing penalties on the fixed and random coefficients. The resulting approach, referred to as regularized PQL, is a computationally efficient...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of the American Statistical Association|
|Access Rights:||Open Access|
|ms-pqrlv7.pdf||Article||304.72 kB||Adobe PDF|
|ms-pqrl-suppmaterialv1.pdf||Supplementary Material||298.98 kB||Adobe PDF|
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