activity
20132017
most citedA principal components method to impute missing values for mixed data

14 citations · 19 across the 5 of their papers we have counts for

collaborators

7 papers

stat.ME20171 cited

Empirical Bayes approaches to PageRank type algorithms for rating scientific journals

Jean-Louis Foulley, Gilles Celeux, Julie Josse

Following criticisms against the journal Impact Factor, new journal influence scores have been developed such as the Eigenfactor or the Prestige Scimago Journal Rank. They are base…

stat.ME20173 cited

Some discussions on the Read Paper "Beyond subjective and objective in statistics" by A. Gelman and C. Hennig

Gilles Celeux, Jack Jewson, Julie Josse +2

This note is a collection of several discussions of the paper "Beyond subjective and objective in statistics", read by A. Gelman and C. Hennig to the Royal Statistical Society on A…

stat.ME2016

Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood

Piotr Sobczyk, Malgorzata Bogdan, Julie Josse

We discuss the problem of estimating the number of principal components in Principal Com- ponents Analysis (PCA). Despite of the importance of the problem and the multitude of solu…

stat.ME2016

Multiple Correspondence Analysis & the Multilogit Bilinear Model

William Fithian, Julie Josse

Multiple Correspondence Analysis (MCA) is a dimension reduction method which plays a large role in the analysis of tables with categorical nominal variables such as survey data. Th…

stat.ME2016

Multinomial Multiple Correspondence Analysis

Patrick J. F. Groenen, Julie Josse

Relations between categorical variables can be analyzed conveniently by multiple correspondence analysis (MCA). %It is well suited to discover relations that may exist between cate…

stat.ME20151 cited

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…