14 citations · 17 across the 8 of their papers we have counts for
5 papers · 1 filter
Nonparametric imputation by data depth
Pavlo Mozharovskyi, Julie Josse, Francois Husson
We present single imputation method for missing values which borrows the idea of data depth---a measure of centrality defined for an arbitrary point of a space with respect to a pr…
MIMCA: Multiple imputation for categorical variables with multiple correspondence analysis
Vincent Audigier, François Husson, Julie Josse
We propose a multiple imputation method to deal with incomplete categorical data. This method imputes the missing entries using the principal components method dedicated to categor…
Confidence Areas for Fixed-Effects PCA
Julie Josse, Stefan Wager, François Husson
PCA is often used to visualize data when the rows and the columns are both of interest. In such a setting there is a lack of inferential methods on the PCA output. We study the asy…
Multiple imputation for continuous variables using a Bayesian principal component analysis
Vincent Audigier, François Husson, Julie Josse
We propose a multiple imputation method based on principal component analysis (PCA) to deal with incomplete continuous data. To reflect the uncertainty of the parameters from one i…
Regularised PCA to denoise and visualise data
Marie Verbanck, Julie Josse, François Husson
Principal component analysis (PCA) is a well-established method commonly used to explore and visualise data. A classical PCA model is the fixed effect model where data are generate…