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Department of Pure Mathematics and Mathematical Statistics

 

Professor of Mathematical Statistics

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

 

Publications

Consistent Inversion of NoisyNon-Abelian X-RayTransforms
F Monard, R Nickl, GP Paternain
– Communications on Pure and Applied Mathematics
(2020)
cpa.21942
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
Uncertainty Quantification for Matrix Compressed Sensing and Quantum Tomography Problems
A Carpentier, J Eisert, D Gross, R Nickl
– pp
(2019)
3,
385
Efficient nonparametric Bayesian inference for $X$-ray transforms
F Monard, R Nickl, GP Paternain
– The Annals of Statistics
(2019)
47,
1113
A Conversation with Dick Dudley
V Koltchinskii, R Nickl, P Rigollet
– Statistical Science
(2019)
34,
169
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Room

D2.05

Telephone

01223 765020