Matthieu Marbac

436 total citations
21 papers, 236 citations indexed

About

Matthieu Marbac is a scholar working on Artificial Intelligence, Statistics and Probability and Signal Processing. According to data from OpenAlex, Matthieu Marbac has authored 21 papers receiving a total of 236 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 8 papers in Statistics and Probability and 3 papers in Signal Processing. Recurrent topics in Matthieu Marbac's work include Bayesian Methods and Mixture Models (14 papers), Statistical Methods and Bayesian Inference (7 papers) and Advanced Clustering Algorithms Research (7 papers). Matthieu Marbac is often cited by papers focused on Bayesian Methods and Mixture Models (14 papers), Statistical Methods and Bayesian Inference (7 papers) and Advanced Clustering Algorithms Research (7 papers). Matthieu Marbac collaborates with scholars based in France, Canada and Spain. Matthieu Marbac's co-authors include Mohammed Sedki, Christophe Biernacki, Paul D. McNicholas, Sandrine Lioret, Patricia Dargent‐Molina, Maxime Cornet, Marie‐Christine Boutron‐Ruault, Sabine Plancoulaine, Cecília Gomes and Marie‐Aline Charles and has published in prestigious journals such as Bioinformatics, International Journal of Behavioral Nutrition and Physical Activity and Journal of the Royal Statistical Society Series C (Applied Statistics).

In The Last Decade

Matthieu Marbac

21 papers receiving 226 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Matthieu Marbac France 9 90 42 33 24 23 21 236
Mohammed Sedki France 8 64 0.7× 41 1.0× 29 0.9× 13 0.5× 18 0.8× 14 179
Jiehuan Sun United States 11 63 0.7× 31 0.7× 25 0.8× 7 0.3× 20 0.9× 29 461
Michail Papathomas United Kingdom 6 120 1.3× 30 0.7× 96 2.9× 6 0.3× 82 3.6× 25 353
Vincent Audigier France 6 59 0.7× 11 0.3× 97 2.9× 13 0.5× 13 0.6× 9 324
Jean-Marie Monnez France 7 31 0.3× 25 0.6× 34 1.0× 5 0.2× 42 1.8× 21 309
Nadir Ammour France 7 79 0.9× 94 2.2× 10 0.3× 4 0.2× 8 0.3× 11 333
Emily Slade United States 9 34 0.4× 47 1.1× 15 0.5× 3 0.1× 9 0.4× 42 315
Diana Kelmansky Argentina 10 38 0.4× 39 0.9× 43 1.3× 4 0.2× 4 0.2× 25 309
Erica Tavazzi Italy 7 46 0.5× 8 0.2× 14 0.4× 12 0.5× 4 0.2× 18 178
Carla Moreira Portugal 15 58 0.6× 50 1.2× 139 4.2× 2 0.1× 43 1.9× 28 388

Countries citing papers authored by Matthieu Marbac

Since Specialization
Citations

This map shows the geographic impact of Matthieu Marbac'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 Matthieu Marbac with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthieu Marbac more than expected).

Fields of papers citing papers by Matthieu Marbac

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthieu Marbac. 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 Matthieu Marbac. The network helps show where Matthieu Marbac may publish in the future.

Co-authorship network of co-authors of Matthieu Marbac

This figure shows the co-authorship network connecting the top 25 collaborators of Matthieu Marbac. A scholar is included among the top collaborators of Matthieu Marbac 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 Matthieu Marbac. Matthieu Marbac is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Marbac, Matthieu, et al.. (2024). Model-based clustering with missing not at random data. Statistics and Computing. 34(4). 3 indexed citations
2.
Marbac, Matthieu, et al.. (2024). Full Model Estimation for Non-Parametric Multivariate Finite Mixture Models. SPIRE - Sciences Po Institutional REpository. 1 indexed citations
3.
Marbac, Matthieu, et al.. (2023). Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components. Advances in Data Analysis and Classification. 2 indexed citations
4.
Frédette, Marc, et al.. (2023). Translation-invariant functional clustering on COVID-19 deaths adjusted on population risk factors. Journal of the Royal Statistical Society Series C (Applied Statistics). 72(2). 387–413. 1 indexed citations
5.
Dumas, Orianne, Annabelle Bédard, Matthieu Marbac, et al.. (2021). Household Cleaning and Poor Asthma Control Among Elderly Women. The Journal of Allergy and Clinical Immunology In Practice. 9(6). 2358–2365.e4. 10 indexed citations
6.
Marbac, Matthieu, et al.. (2020). Detecting spatial clusters in functional data: new scan statistic\n approaches. arXiv (Cornell University). 6 indexed citations
7.
Gomes, Cecília, Matthieu Marbac, Mohammed Sedki, et al.. (2020). Clusters of diet, physical activity, television exposure and sleep habits and their association with adiposity in preschool children: the EDEN mother-child cohort. International Journal of Behavioral Nutrition and Physical Activity. 17(1). 20–20. 24 indexed citations
8.
Biernacki, Christophe, et al.. (2020). Gaussian-Based Visualization of Gaussian and Non-Gaussian-Based Clustering. Journal of Classification. 38(1). 129–157. 5 indexed citations
9.
Marbac, Matthieu, et al.. (2019). Mixture of hidden Markov models for accelerometer data. HAL (Le Centre pour la Communication Scientifique Directe). 4 indexed citations
10.
Marbac, Matthieu, et al.. (2019). Variable Selection for Mixed Data Clustering: Application in Human Population Genomics. Journal of Classification. 37(1). 124–142. 14 indexed citations
11.
Biernacki, Christophe, et al.. (2019). Gaussian-Based Visualization of Gaussian and Non-Gaussian Model-Based Clustering [R package ClusVis version 1.2.0]. 1 indexed citations
12.
Marbac, Matthieu, Mohammed Sedki, Marie‐Christine Boutron‐Ruault, & Orianne Dumas. (2018). Patterns of cleaning product exposures using a novel clustering approach for data with correlated variables. Annals of Epidemiology. 28(8). 563–569.e6. 8 indexed citations
13.
Marbac, Matthieu & Mohammed Sedki. (2018). VarSelLCM: an R/C++ package for variable selection in model-based clustering of mixed-data with missing values. Bioinformatics. 35(7). 1255–1257. 39 indexed citations
14.
Marbac, Matthieu, et al.. (2017). Model‐based clustering for spatiotemporal data on air quality monitoring. Environmetrics. 28(3). 16 indexed citations
15.
Marbac, Matthieu & Mohammed Sedki. (2017). A family of block-wise one-factor distributions for modeling high-dimensional binary data. Computational Statistics & Data Analysis. 114. 130–145. 1 indexed citations
16.
Marbac, Matthieu, et al.. (2017). Model-based clustering of Gaussian copulas for mixed data. Communication in Statistics- Theory and Methods. 46(23). 11635–11656. 24 indexed citations
17.
Marbac, Matthieu & Mohammed Sedki. (2016). Variable selection for model-based clustering using the integrated complete-data likelihood. Statistics and Computing. 27(4). 1049–1063. 49 indexed citations
18.
Marbac, Matthieu, et al.. (2016). Latent class model with conditional dependency per modes to cluster categorical data. Advances in Data Analysis and Classification. 10(2). 183–207. 1 indexed citations
19.
Marbac, Matthieu, et al.. (2015). Model-based clustering for conditionally correlated categorical data. HAL (Le Centre pour la Communication Scientifique Directe). 4 indexed citations
20.
Marbac, Matthieu, et al.. (2015). Model-Based Clustering for Conditionally Correlated Categorical Data. Journal of Classification. 32(2). 145–175. 21 indexed citations

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.

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