8 citations · 11 across the 6 of their papers we have counts for
6 papers
Estimation of ratios of normalizing constants using stochastic approximation : the SARIS algorithm
Tom Guédon, Charlotte Baey, Estelle Kuhn
Computing ratios of normalizing constants plays an important role in statistical modeling. Two important examples are hypothesis testing in latent variables models, and model compa…
Efficient preconditioned stochastic gradient descent for estimation in latent variable models
Charlotte Baey, Maud Delattre, Estelle Kuhn +2
Latent variable models are powerful tools for modeling complex phenomena involving in particular partially observed data, unobserved variables or underlying complex unknown structu…
Bootstrap test procedure for variance components in nonlinear mixed effects models in the presence of nuisance parameters and a singular Fisher Information Matrix
Tom Guédon, Charlotte Baey, Estelle Kuhn
We examine the problem of variance components testing in general mixed effects models using the likelihood ratio test. We account for the presence of nuisance parameters, i.e. the…
Calibration of a bumble bee foraging model using Approximate Bayesian Computation
Charlotte Baey, Henrik G. Smith, Maj Rundlöf +3
1. Challenging calibration of complex models can be approached by using prior knowledge on the parameters. However, the natural choice of Bayesian inference can be computationally…
varTestnlme: an R package for Variance Components Testing in Linear and Nonlinear Mixed-effects Models
Charlotte Baey, Estelle Kuhn
The issue of variance components testing arises naturally when building mixed-effects models, to decide which effects should be modeled as fixed or random. While tests for fixed ef…
Likelihood ratio test for variance components in nonlinear mixed effects models
Charlotte Baey, Paul-Henry Cournède, Estelle Kuhn
Mixed effects models are widely used to describe heterogeneity in a population. A crucial issue when adjusting such a model to data consists in identifying fixed and random effects…