14 citations · 17 across the 8 of their papers we have counts for
8 papers
Multilingual textual data: an approach through multiple factor analysis
Kostov Blechin, Alvarez-Esteban Ramón, Bécue-Bertaut Mónica +1
This paper focuses on the analysis of open-ended questions answered in different languages. Closed-ended questions, called contextual variables, are asked to all respondents in ord…
Imputation of mixed data with multilevel singular value decomposition
François Husson, Julie Josse, Balasubramanian Narasimhan +1
Statistical analysis of large data sets offers new opportunities to better understand many processes. Yet, data accumulation often implies relaxing acquisition procedures or compou…
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…
A principal components method to impute missing values for mixed data
Vincent Audigier, François Husson, Julie Josse
We propose a new method to impute missing values in mixed datasets. It is based on a principal components method, the factorial analysis for mixed data, which balances the influenc…
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…