Agnès Lagnoux

844 total citations
21 papers, 485 citations indexed

About

Agnès Lagnoux is a scholar working on Statistics, Probability and Uncertainty, Artificial Intelligence and Statistics and Probability. According to data from OpenAlex, Agnès Lagnoux has authored 21 papers receiving a total of 485 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Statistics, Probability and Uncertainty, 7 papers in Artificial Intelligence and 6 papers in Statistics and Probability. Recurrent topics in Agnès Lagnoux's work include Probabilistic and Robust Engineering Design (7 papers), Statistical Methods and Inference (4 papers) and Gaussian Processes and Bayesian Inference (4 papers). Agnès Lagnoux is often cited by papers focused on Probabilistic and Robust Engineering Design (7 papers), Statistical Methods and Inference (4 papers) and Gaussian Processes and Bayesian Inference (4 papers). Agnès Lagnoux collaborates with scholars based in France, Vietnam and United States. Agnès Lagnoux's co-authors include Thierry Klein, Alexandre Janon, Fabrice Gamboa, Clémentine Prieur, Maëlle Nodet, François Bachoc, Christophe Prieur, Loïc Brevault, Jérôme Morio and Mathieu Balesdent and has published in prestigious journals such as Bioinformatics, Reliability Engineering & System Safety and Journal of the Royal Statistical Society Series C (Applied Statistics).

In The Last Decade

Agnès Lagnoux

19 papers receiving 463 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Agnès Lagnoux France 10 313 103 79 75 73 21 485
Sifeng Bi China 16 444 1.4× 378 3.7× 111 1.4× 41 0.5× 58 0.8× 44 718
Wolfgang Betz Germany 10 599 1.9× 489 4.7× 109 1.4× 85 1.1× 50 0.7× 17 907
Chunyan Ling China 15 591 1.9× 272 2.6× 264 3.3× 53 0.7× 129 1.8× 39 733
Loïc Brevault France 12 242 0.8× 56 0.5× 161 2.0× 17 0.2× 59 0.8× 32 468
M. Grigoriu United States 12 367 1.2× 218 2.1× 77 1.0× 17 0.2× 21 0.3× 32 495
Chao Dang China 20 822 2.6× 520 5.0× 210 2.7× 74 1.0× 72 1.0× 55 1.0k
Peter A. Parker United States 15 393 1.3× 49 0.5× 80 1.0× 222 3.0× 172 2.4× 64 825
C. F. Jeff Wu United States 10 81 0.3× 126 1.2× 70 0.9× 13 0.2× 54 0.7× 22 373
Rui Tuo United States 13 215 0.7× 17 0.2× 246 3.1× 53 0.7× 122 1.7× 33 519

Countries citing papers authored by Agnès Lagnoux

Since Specialization
Citations

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

Fields of papers citing papers by Agnès Lagnoux

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Agnès Lagnoux

This figure shows the co-authorship network connecting the top 25 collaborators of Agnès Lagnoux. A scholar is included among the top collaborators of Agnès Lagnoux 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 Agnès Lagnoux. Agnès Lagnoux 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.
Bachoc, François & Agnès Lagnoux. (2024). Posterior contraction rates for constrained deep Gaussian processes in density estimation and classification. Communication in Statistics- Theory and Methods. 54(3). 774–811. 1 indexed citations
2.
Gamboa, Fabrice, Pierre A. Gremaud, Thierry Klein, & Agnès Lagnoux. (2022). Global sensitivity analysis: A novel generation of mighty estimators based on rank statistics. Bernoulli. 28(4). 18 indexed citations
3.
Klein, Thierry, Agnès Lagnoux, & P. Petit. (2022). Deviation results for sparse tables in hashing with linear probing. Probability Theory and Related Fields. 183(3-4). 871–908.
4.
Morio, Jérôme, et al.. (2021). Reliability-oriented sensitivity analysis in presence of data-driven epistemic uncertainty. Reliability Engineering & System Safety. 215. 107733–107733. 30 indexed citations
5.
Azäis, Jean‐Marc, et al.. (2020). Semi-parametric estimation of the variogram scale parameter of a Gaussian process with stationary increments. ESAIM Probability and Statistics. 24. 842–882. 1 indexed citations
6.
Bachoc, François & Agnès Lagnoux. (2020). Fixed-domain asymptotic properties of maximum composite likelihood estimators for Gaussian processes. Journal of Statistical Planning and Inference. 209. 62–75. 3 indexed citations
7.
Bachoc, François, et al.. (2019). Maximum likelihood estimation for Gaussian processes under inequality constraints. Electronic Journal of Statistics. 13(2). 13 indexed citations
8.
Bachoc, François, et al.. (2018). Maximum likelihood estimation for Gaussian processes under inequality constraints. arXiv (Cornell University). 1 indexed citations
9.
Lagnoux, Agnès, et al.. (2018). Probability density function of the local score position. Stochastic Processes and their Applications. 129(10). 3664–3689.
10.
Bachoc, François, et al.. (2017). Cross-validation estimation of covariance parameters under fixed-domain asymptotics. Journal of Multivariate Analysis. 160. 42–67. 12 indexed citations
11.
Lagnoux, Agnès & Pascal Lezaud. (2017). Multilevel branching and splitting algorithm for estimating rare event probabilities. Simulation Modelling Practice and Theory. 72. 150–167. 2 indexed citations
12.
Lagnoux, Agnès, et al.. (2016). Statistical significance based on length and position of the local score in a model of i.i.d. sequences. Bioinformatics. 33(5). 654–660. 3 indexed citations
13.
Lagnoux, Agnès, et al.. (2015). Probability that the maximum of the reflected Brownian motion over a finite interval $[0,t]$ is achieved by its last zero before $t$.. Electronic Communications in Probability. 20(none). 4 indexed citations
14.
Lagnoux, Agnès, et al.. (2014). Elements related to the largest complete excursion of a reflected BM stopped at a fixed time. Application to local score. Stochastic Processes and their Applications. 124(12). 4202–4223. 4 indexed citations
15.
Gamboa, Fabrice, Alexandre Janon, Thierry Klein, & Agnès Lagnoux. (2014). Sensitivity analysis for multidimensional and functional outputs. Electronic Journal of Statistics. 8(1). 69 indexed citations
16.
Gamboa, Fabrice, Alexandre Janon, Thierry Klein, & Agnès Lagnoux. (2013). Sensitivity indices for multivariate outputs. Comptes Rendus Mathématique. 351(7-8). 307–310. 62 indexed citations
17.
Janon, Alexandre, Thierry Klein, Agnès Lagnoux, Maëlle Nodet, & Clémentine Prieur. (2013). Asymptotic normality and efficiency of two Sobol index estimators. ESAIM Probability and Statistics. 18. 342–364. 151 indexed citations
18.
Fort, Jean‐Claude, Thierry Klein, Agnès Lagnoux, & Béatrice Laurent. (2013). Estimation of the Sobol indices in a linear functional multidimensional model. Journal of Statistical Planning and Inference. 143(9). 1590–1605. 9 indexed citations
19.
Azäis, Jean‐Marc, et al.. (2010). Simultaneous Confidence Bands in Curve Prediction Applied to Load Curves. Journal of the Royal Statistical Society Series C (Applied Statistics). 59(5). 889–904. 5 indexed citations
20.
Lagnoux, Agnès. (2005). RARE EVENT SIMULATION. Probability in the Engineering and Informational Sciences. 20(1). 45–66. 64 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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