4 citations · 4 across the 3 of their papers we have counts for
3 papers
Information criteria exploiting latent structure for model selection in Structural Equation Models
Marion Naveau, Magalie Houée-Bigot, Matthieu Marbac +2
Structural equation models (SEM) are widely used to describe dependency structures between latent variables, making model selection a key issue in many applications. Existing infor…
Posterior contraction rates in a sparse non-linear mixed-effects model
Marion Naveau, Maud Delattre, Laure Sansonnet
Recent works have shown an interest in investigating the frequentist asymptotic properties of Bayesian procedures for high-dimensional linear models under sparsity constraints. How…
Bayesian high-dimensional covariate selection in non-linear mixed-effects models using the SAEM algorithm
Marion Naveau, Guillaume Kon Kam King, Renaud Rincent +2
High-dimensional variable selection, with many more covariates than observations, is widely documented in standard regression models, but there are still few tools to address it in…