- Koenig, C., Munk, A., Werner, F., (2023).
Multiscale scanning with nuisance parameters arxiv.org/abs/2307.13301 . Submitted. - Kulaitis, G., Munk, A., Werner, F. (2021).
What is resolution? A statistical minimax testing perspective on super-resolution microscopy. arXiv:2005.07450 The Annals of Statistics, 49 (4), 2292-2312. - Munk, A., Proksch, K., Li, H., Werner, F. (2020).
Photonic Imaging with Statistical Guarantees: From Multiscale Testing to Multiscale Estimation. Nanoscale Photonic Imaging, 283-312. - del Alamo, M., Li H., Munk A., Werner F. (2020).
Variational multiscale nonparametric regression: Algorithms and implementation arXiv:2010.10660 Algorithms, 13 (11), 296. - Enikeeva, F., Munk, A., Pohlmann, M, Werner, F. (2020).
Bump detection in the presence of dependency: Does it ease or does it load? arXiv:1906.08017 Bernoulli, 26 (4), 3280-3310. - König, C., Munk, A., Werner, F. (2020).
Multidimensional multiscale scanning in exponential families: Limit theory and statistical consequences. arXiv:1802.07995 Annals of Statistics, 48 (2), 655-678. - Li, Housen, Werner, Frank (2020).
Empirical Risk Minimization as Parameter Choice Rule for General Linear Regularization Methods. arXiv:1703.07809 Annales de lInstitut Henri Poincaré, 56(1), 405-427. - Werner, F. and Hofmann, B. (2019).
Convergence Analysis of (Statistical) Inverse Problems under Conditional Stability Estimates Inverse Problems, 36, 015004. - Werner, F. (2018).
Adaptivity and oracle inequalities in linear statistical inverse problems: a (numerical) Survey. In: Hofmann B., Leitão A., Zubelli J. (eds) New Trends in Parameter Identification for Mathematical Models. Trends in Mathematics. Birkhäuser, Cham., 291-316. - Proksch, K., Werner, F., Munk, A. (2018).
Multiscale scanning in inverse problems. arXiv:1611.04537 The Annals of Statistics, 46 (6B), 3569-3602. - Enikeeva, F., Munk, A., Werner, F. (2018).
Bump detection in heterogeneous Gaussian regression. arXiv:1504.07390 Bernoulli, 24, 1266-1306. - Hohage, T., Werner, F. (2016).
Inverse Problems with Poisson Data: statistical regularization theory, applications and algorithms. Inverse Problems, 32, 093001 (56pp). - König, C., Werner, F., Hohage, T (2016).
Convergence Rates for Exponentially Ill-Posed Inverse Problems with Impulsive Noise. SIAM Journal on Numerical Analysis, 54(1), 341-360. - Munk, A., Werner, F. (2015).
Discussion of Hypotheses testing by convex optimization Electron. J. Statist., 9, 1720-1722.
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