Vincent Audigier

607 total citations
9 papers, 324 citations indexed

About

Vincent Audigier is a scholar working on Statistics and Probability, Artificial Intelligence and Sociology and Political Science. According to data from OpenAlex, Vincent Audigier has authored 9 papers receiving a total of 324 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Statistics and Probability, 3 papers in Artificial Intelligence and 2 papers in Sociology and Political Science. Recurrent topics in Vincent Audigier's work include Statistical Methods and Bayesian Inference (6 papers), Bayesian Methods and Mixture Models (3 papers) and Statistical Methods and Inference (2 papers). Vincent Audigier is often cited by papers focused on Statistical Methods and Bayesian Inference (6 papers), Bayesian Methods and Mixture Models (3 papers) and Statistical Methods and Inference (2 papers). Vincent Audigier collaborates with scholars based in France, Netherlands and Switzerland. Vincent Audigier's co-authors include Julie Josse, François Husson, Matthieu Resche‐Rigon, Thomas P. A. Debray, Matteo Quartagno, Stef van Buuren, James R. Carpenter, Shahab Jolani, Ian R. White and Mourad Benyamina and has published in prestigious journals such as Frontiers in Immunology, Statistics in Medicine and Statistical Science.

In The Last Decade

Vincent Audigier

8 papers receiving 317 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Vincent Audigier France	6	97	59	56	24	23	9	324
David E. Morris United Kingdom	14	26 0.3×	28 0.5×	50 0.9×	70 2.9×	26 1.1×	44	681
Anna Gottard Italy	9	63 0.6×	30 0.5×	29 0.5×	7 0.3×	8 0.3×	26	256
Sudhir Jadhav India	8	18 0.2×	28 0.5×	35 0.6×	6 0.3×	18 0.8×	32	455
Luis León‐Novelo United States	14	83 0.9×	30 0.5×	121 2.2×	24 1.0×	7 0.3×	47	492
Yeongjin Gwon United States	9	147 1.5×	13 0.2×	20 0.4×	11 0.5×	20 0.9×	30	387
T. W. F. Stroud Canada	10	203 2.1×	58 1.0×	21 0.4×	10 0.4×	19 0.8×	46	461
Basílio de Bragança Pereira Brazil	14	67 0.7×	23 0.4×	143 2.6×	16 0.7×	18 0.8×	73	606
Heidi Seibold Germany	11	90 0.9×	62 1.1×	15 0.3×	2 0.1×	14 0.6×	21	293
Pralay Senchaudhuri United States	16	281 2.9×	63 1.1×	21 0.4×	2 0.1×	34 1.5×	26	547
W. Molenaar Netherlands	8	56 0.6×	26 0.4×	53 0.9×	29 1.2×	18 0.8×	18	540

Countries citing papers authored by Vincent Audigier

Since Specialization

Citations

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

Fields of papers citing papers by Vincent Audigier

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vincent Audigier

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

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Efthimiou, Orestis, et al.. (2023). Multiple imputation of incomplete multilevel data using Heckman selection models. Statistics in Medicine. 43(3). 514–533. 1 indexed citations

2.

Audigier, Vincent, et al.. (2022). Clustering with missing data: which equivalent for Rubin’s rules?. Advances in Data Analysis and Classification. 17(3). 623–657. 4 indexed citations

3.

Moins‐Teisserenc, Hélène, Vincent Audigier, Mourad Benyamina, et al.. (2021). Severe Altered Immune Status After Burn Injury Is Associated With Bacterial Infection and Septic Shock. Frontiers in Immunology. 12. 586195–586195. 54 indexed citations

4.

Audigier, Vincent & Matthieu Resche‐Rigon. (2019). Multiple Imputation by Chained Equations with Multilevel Data [R package micemd version 1.6.0]. 6 indexed citations

5.

Bar‐Hen, Avner & Vincent Audigier. (2018). An ensemble learning method for variable selection: application to high\n dimensional data and missing values. arXiv (Cornell University).

6.

Audigier, Vincent, Ian R. White, Shahab Jolani, et al.. (2018). Multiple Imputation for Multilevel Data with Continuous and Binary Variables. Statistical Science. 33(2). 91 indexed citations

7.

Audigier, Vincent, François Husson, & Julie Josse. (2016). MIMCA: multiple imputation for categorical variables with multiple correspondence analysis. Statistics and Computing. 27(2). 501–518. 29 indexed citations

8.

Audigier, Vincent, François Husson, & Julie Josse. (2015). Multiple imputation for continuous variables using a Bayesian principal component analysis. Journal of Statistical Computation and Simulation. 86(11). 2140–2156. 47 indexed citations

9.

Audigier, Vincent, François Husson, & Julie Josse. (2014). A principal component method to impute missing values for mixed data. Advances in Data Analysis and Classification. 10(1). 5–26. 92 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.

Explore authors with similar magnitude of impact