2 citations · 3 across the 3 of their papers we have counts for
3 papers
stat.ME2021
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…
stat.ME2020★ 1 cited
Sparse Correspondence Analysis for Contingency Tables
Ruiping Liu, Ndeye Niang, Gilbert Saporta +1
Since the introduction of the lasso in regression, various sparse methods have been developed in an unsupervised context like sparse principal component analysis (s-PCA), sparse ca…
stat.ME2020★ 2 cited
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…