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Department of Pure Mathematics and Mathematical Statistics

 

Professor of Mathematical Statistics

Research Interests: Mathematical Statistics; specifically high-dimensional inference, Bayesian nonparametrics, statistics for PDEs and inverse problems, empirical process theory.

 

Publications

STATISTICAL GUARANTEES for BAYESIAN UNCERTAINTY QUANTIFICATION in NONLINEAR INVERSE PROBLEMS with GAUSSIAN PROCESS PRIORS
F Monard, R Nickl, GP Paternain
– Annals of Statistics
(2021)
49,
3255
On some information-theoretic aspects of non-linear statistical inverse problems
R Nickl, G Paternain
(2021)
On log-concave approximations of high-dimensional posterior measures and stability properties in non-linear inverse problems
J Bohr, R Nickl
(2021)
Consistent Inversion of NoisyNon-Abelian X-RayTransforms
F Monard, R Nickl, GP Paternain
– Communications on Pure and Applied Mathematics
(2020)
74,
1045
Consistency of Bayesian inference with Gaussian process priors in an elliptic inverse problem
M Giordano, R Nickl
– Inverse Problems
(2020)
36,
085001
Nonparametric statistical inference for drift vector fields of multi-dimensional diffusions
R Nickl, K Ray
– Annals of Statistics
(2020)
48,
1383
Bernstein-von Mises theorems for statistical inverse problems I: Schrödinger equation
R Nickl
– Journal of the European Mathematical Society
(2020)
22,
2697
Convergence Rates for Penalized Least Squares Estimators in PDE Constrained Regression Problems
R Nickl, S Van De Geer, S Wang
– SIAM/ASA Journal on Uncertainty Quantification
(2020)
8,
374
Efficient estimation of linear functionals of principal components
V Koltchinskii, M Loffler, R Nickl
– The Annals of Statistics
(2020)
48,
464
Efficient estimation of linear functionals of principal components
V Koltchinskii, M Löffler, R Nickl
– Annals of Statistics
(2020)
48,
464
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Room

D2.05

Telephone

01223 765020