Richard Nickl

1.7k total citations
36 papers, 749 citations indexed

About

Richard Nickl is a scholar working on Statistics and Probability, Artificial Intelligence and Mathematical Physics. According to data from OpenAlex, Richard Nickl has authored 36 papers receiving a total of 749 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Statistics and Probability, 15 papers in Artificial Intelligence and 6 papers in Mathematical Physics. Recurrent topics in Richard Nickl's work include Statistical Methods and Inference (20 papers), Bayesian Methods and Mixture Models (10 papers) and Markov Chains and Monte Carlo Methods (7 papers). Richard Nickl is often cited by papers focused on Statistical Methods and Inference (20 papers), Bayesian Methods and Mixture Models (10 papers) and Markov Chains and Monte Carlo Methods (7 papers). Richard Nickl collaborates with scholars based in United Kingdom, United States and Austria. Richard Nickl's co-authors include Evarist Giné, Karim Lounici, Ismaël Castillo, Benedikt M. Pötscher, Marc Hoffmann, François Monard, Gabriel P. Paternain, Markus Reiß, Adam D. Bull and Sara van de Geer and has published in prestigious journals such as The Annals of Statistics, Communications on Pure and Applied Mathematics and Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences.

In The Last Decade

Richard Nickl

35 papers receiving 719 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Richard Nickl United Kingdom 17 474 261 133 107 102 36 749
Mark G. Low United States 14 581 1.2× 187 0.7× 52 0.4× 113 1.1× 120 1.2× 29 812
F.H. Ruymgaart United States 15 580 1.2× 222 0.9× 263 2.0× 238 2.2× 179 1.8× 90 1.1k
Jianfeng Yao Hong Kong 20 740 1.6× 346 1.3× 244 1.8× 200 1.9× 61 0.6× 90 1.2k
Oleg Lepski France 13 489 1.0× 187 0.7× 27 0.2× 98 0.9× 120 1.2× 28 696
Cristina Butucea France 13 312 0.7× 176 0.7× 36 0.3× 106 1.0× 49 0.5× 41 469
Christopher S. Withers United Kingdom 14 536 1.1× 214 0.8× 81 0.6× 255 2.4× 103 1.0× 159 1.0k
Víctor Peña United States 11 293 0.6× 138 0.5× 134 1.0× 174 1.6× 122 1.2× 38 633
Noureddine El Karoui United States 14 501 1.1× 253 1.0× 73 0.5× 203 1.9× 41 0.4× 32 1.0k
Michael Nussbaum Germany 10 320 0.7× 125 0.5× 39 0.3× 84 0.8× 79 0.8× 20 482
Ilya Molchanov Switzerland 20 445 0.9× 206 0.8× 164 1.2× 150 1.4× 400 3.9× 103 1.2k

Countries citing papers authored by Richard Nickl

Since Specialization
Citations

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

Fields of papers citing papers by Richard Nickl

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Richard Nickl

This figure shows the co-authorship network connecting the top 25 collaborators of Richard Nickl. A scholar is included among the top collaborators of Richard Nickl 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 Richard Nickl. Richard Nickl 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.
Nickl, Richard, et al.. (2025). Bayesian nonparametric inference in McKean–Vlasov models. The Annals of Statistics. 53(1). 2 indexed citations
2.
Nickl, Richard, et al.. (2024). On log-concave approximations of high-dimensional posterior measures and stability properties in non-linear inverse problems. Annales de l Institut Henri Poincaré Probabilités et Statistiques. 60(4). 2 indexed citations
3.
Nickl, Richard & Edriss S. Titi. (2024). On posterior consistency of data assimilation with Gaussian process priors: The 2D-Navier–Stokes equations. The Annals of Statistics. 52(4). 2 indexed citations
4.
Nickl, Richard. (2024). Consistent inference for diffusions from low frequency measurements. The Annals of Statistics. 52(2). 3 indexed citations
5.
Nickl, Richard. (2023). Bayesian Non-linear Statistical Inverse Problems. 10 indexed citations
6.
Nickl, Richard, et al.. (2022). On polynomial-time computation of high-dimensional posterior measures by Langevin-type algorithms. Journal of the European Mathematical Society. 26(3). 1031–1112. 10 indexed citations
7.
Nickl, Richard, Judith Rousseau, & Aad van der Vaart. (2022). Foundations of Bayesian Inference for Complex Statistical Models. Oberwolfach Reports. 18(2). 1191–1208.
8.
Monard, François, Richard Nickl, & Gabriel P. Paternain. (2021). Statistical guarantees for Bayesian uncertainty quantification in nonlinear inverse problems with Gaussian process priors. The Annals of Statistics. 49(6). 20 indexed citations
9.
Giné, Evarist & Richard Nickl. (2021). Mathematical Foundations of Infinite-Dimensional Statistical Models. Cambridge University Press eBooks. 18 indexed citations
10.
Nickl, Richard, et al.. (2020). On statistical Calderón problems. 2(2). 165–216. 27 indexed citations
11.
Monard, François, Richard Nickl, & Gabriel P. Paternain. (2020). Consistent Inversion of Noisy Non‐Abelian X‐Ray Transforms. Communications on Pure and Applied Mathematics. 74(5). 1045–1099. 29 indexed citations
12.
Nickl, Richard, et al.. (2020). Convergence Rates for Penalized Least Squares Estimators in PDE Constrained Regression Problems. SIAM/ASA Journal on Uncertainty Quantification. 8(1). 374–413. 19 indexed citations
13.
Nickl, Richard, et al.. (2017). Nonparametric Bayesian posterior contraction rates for discretely observed scalar diffusions. The Annals of Statistics. 45(4). 27 indexed citations
14.
Koltchinskii, Vladimir, Richard Nickl, Sara van de Geer, & Jon A. Wellner. (2016). The mathematical work of Evarist Giné. Stochastic Processes and their Applications. 126(12). 3607–3622. 1 indexed citations
15.
Nickl, Richard & Botond Szabó. (2016). A sharp adaptive confidence ball for self-similar functions. Stochastic Processes and their Applications. 126(12). 3913–3934. 9 indexed citations
16.
Castillo, Ismaël & Richard Nickl. (2014). On the Bernstein–von Mises phenomenon for nonparametric Bayes procedures. The Annals of Statistics. 42(5). 49 indexed citations
17.
Nickl, Richard & Markus Reiß. (2012). A Donsker theorem for Lévy measures. Journal of Functional Analysis. 263(10). 3306–3332. 23 indexed citations
18.
Lounici, Karim & Richard Nickl. (2010). Global uniform risk bounds for wavelet deconvolution estimators. The Annals of Statistics. 39(1). 54 indexed citations
19.
Giné, Evarist & Richard Nickl. (2008). Adaptation on the space of finite signed measures. Mathematical Methods of Statistics. 17(2). 113–122. 1 indexed citations
20.
Nickl, Richard. (2006). Donsker-type theorems for nonparametric maximum likelihood estimators. Probability Theory and Related Fields. 138(3-4). 19 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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