Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
FactoMineR : An R Package for Multivariate Analysis
20087.0k citationsSébastien Lê, Julie Josse et al.Journal of Statistical Softwareprofile →
missMDA: A Package for Handling Missing Values in Multivariate Data Analysis
2016767 citationsJulie Josse, François HussonJournal of Statistical Softwareprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Julie Josse's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Julie Josse with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Julie Josse more than expected).
This network shows the impact of papers produced by Julie Josse. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Julie Josse. The network helps show where Julie Josse may publish in the future.
Co-authorship network of co-authors of Julie Josse
This figure shows the co-authorship network connecting the top 25 collaborators of Julie Josse.
A scholar is included among the top collaborators of Julie Josse based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Julie Josse. Julie Josse is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Josse, Julie, et al.. (2018). Stochastic Approximation EM for Logistic Regression with Missing Values. arXiv (Cornell University).1 indexed citations
12.
Seijo-Pardo, Borja, Amparo Alonso‐Betanzos, Kristin P. Bennett, et al.. (2018). Analysis of imputation bias for feature selection with missing data.. The European Symposium on Artificial Neural Networks.1 indexed citations
Josse, Julie & François Husson. (2016). missMDA: A Package for Handling Missing Values in Multivariate Data Analysis. Journal of Statistical Software. 70(1).767 indexed citations breakdown →
Josse, Julie & Sylvain Sardy. (2013). Selecting thresholding and shrinking parameters with generalized SURE for low rank matrix estimation. arXiv (Cornell University).2 indexed citations
19.
Husson, François, Julie Josse, & Jérôme Pagès. (2010). Analyse de données avec R - Complémentarité des méthodes d'analyse factorielle et de classification. HAL (Le Centre pour la Communication Scientifique Directe).13 indexed citations
20.
Lê, Sébastien, Julie Josse, & François Husson. (2008). FactoMineR : An R Package for Multivariate Analysis. Journal of Statistical Software. 25(1).6978 indexed citations breakdown →
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.