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Statistical Laboratory

Publications

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
Uncertainty Quantification for Matrix Compressed Sensing and Quantum Tomography Problems
A Carpentier, J Eisert, D Gross, R Nickl
– pp
(2019)
3,
385
Uncertainty Quantification for Matrix Compressed Sensing and Quantum Tomography Problems
A Carpentier, J Eisert, D Gross, R Nickl
(2019)
74,
385
Bernstein–von mises theorems for statistical inverse problems II: Compound poisson processes
R Nickl, J Söhl
– Electronic Journal of Statistics
(2019)
13,
3513
Adaptive confidence sets for matrix completion
A Carpentier, O Klopp, M Löffler, R Nickl
– Bernoulli
(2018)
24,
2429
Inference on covariance operators via concentration inequalities: K-sample tests, classification, and clustering via rademacher complexities
AB Kashlak, JAD Aston, R Nickl
– Sankhya: The Indian Journal of Statistics
(2019)
81A,
214
Comments on: High-dimensional simultaneous inference with the bootstrap
M Löffler, R Nickl
– Test
(2017)
26,
731
Nonparametric Bayesian posterior contraction rates for discretely observed scalar diffusions
R Nickl, J Söhl
– Annals of Statistics
(2017)
45,
1664
The mathematical work of Evarist Giné
V Koltchinskii, R Nickl, S van de Geer, JA Wellner
– Stochastic Processes and their Applications
(2016)
126,
3607
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Frontpage talks

06
Mar
Statistics

10
Mar
14:00 - 15:00: Title to be confirmed
Probability

Cambridge Statistics Clinic

Cambridge Statistics Clinic

Research Group

Statistical Laboratory

Room

D2.05

Telephone

01223 765020