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Statistical Laboratory

Publications

Nonparametric statistical inference for drift vector fields of multi-dimensional diffusions
R Nickl, K Ray
– Annals of Statistics
(2020)
48,
1383
Consistency of Bayesian inference with Gaussian process priors in an elliptic inverse problem
M Giordano, R Nickl
– Inverse Problems
(2020)
Efficient estimation of linear functionals of principal components
V Koltchinskii, M Loffler, R Nickl
– The Annals of Statistics
(2020)
48,
464
Bernstein–von Mises theorems for statistical inverse problems I: Schrödinger equation
R Nickl
– Journal of the European Mathematical Society
(2020)
22,
2697
Efficient estimation of linear functionals of principal components
V Koltchinskii, M Löffler, R Nickl
– Annals of Statistics
(2020)
48,
464
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 nonparametric Bayesian inference for $X$-ray transforms
F Monard, R Nickl, GP Paternain
– Annals of Statistics
(2019)
47,
1113
A Conversation with Dick Dudley
V Koltchinskii, R Nickl, P Rigollet
– Statistical Science
(2019)
34,
169
Uncertainty Quantification for Matrix Compressed Sensing and Quantum Tomography Problems
A Carpentier, J Eisert, D Gross, R Nickl
(2019)
74,
385
Uncertainty Quantification for Matrix Compressed Sensing and Quantum Tomography Problems
A Carpentier, J Eisert, D Gross, R Nickl
– pp
(2019)
3,
385
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Cambridge Statistics Clinic

Research Group

Statistical Laboratory

Room

D2.05

Telephone

01223 765020