177 citations · 197 across the 9 of their papers we have counts for
9 papers
A simple Bayesian model to estimate proportions and ratios from count data with a hierarchical error structure with an application to droplet digital PCR experiments
Elyas Mouhou, Vincent Audigier, Josselin Noirel
Experimental designs with hierarchically-structured errors are pervasive in many biomedical areas; it is important to take into account this hierarchical architecture in order to a…
Multiple imputation of incomplete multilevel data using Heckman selection models
Johanna Muñoz, Matthias Egger, Orestis Efthimiou +3
Missing data is a common problem in medical research, and is commonly addressed using multiple imputation. Although traditional imputation methods allow for valid statistical infer…
Clustering with missing data: which imputation model for which cluster analysis method?
Vincent Audigier, Ndèye Niang, Matthieu Resche-Rigon
Multiple imputation (MI) is a popular method for dealing with missing values. One main advantage of MI is to separate the imputation phase and the analysis one. However, both are r…
Clustering with missing data: which equivalent for Rubin's rules?
Vincent Audigier, Ndèye Niang
Multiple imputation (MI) is a popular method for dealing with missing values. However, the suitable way for applying clustering after MI remains unclear: how to pool partitions? Ho…
An ensemble learning method for variable selection: application to high dimensional data and missing values
Avner Bar-Hen, Vincent Audigier
Standard approaches for variable selection in linear models are not tailored to deal properly with high-dimensional and incomplete data. Currently, methods dedicated to high-dimens…
Multiple imputation for multilevel data with continuous and binary variables
Vincent Audigier, Ian R. White, Shahab Jolani +5
We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared f…