Inverse Problems with Poisson data: statistical regularization theory, applications and algorithms T Hohage, F Werner Inverse Problems 32 (9), 093001 (56pp), 2016 | 56 | 2016 |

Iteratively regularized Newton-type methods for general data misfit functionals and applications to Poisson data T Hohage, F Werner Numerische Mathematik 123 (4), 745-779, 2013 | 52 | 2013 |

Convergence rates in expectation for Tikhonov-type regularization of inverse problems with Poisson data F Werner, T Hohage Inverse Problems 28 (10), 104004, 2012 | 49 | 2012 |

Convergence Rates for Exponentially Ill-Posed Inverse Problems with Impulsive Noise C König, F Werner, T Hohage SIAM Journal on Numerical Analysis 54 (1), 341-360, 2016 | 24 | 2016 |

Multiscale scanning in inverse problems K Proksch, F Werner, A Munk The Annals of Statistics 46 (6B), 3569-3602, 2018 | 20 | 2018 |

Convergence Rates for Inverse Problems with Impulsive Noise T Hohage, F Werner SIAM Journal on Numerical Analysis 52 (3), 1203-1221, 2014 | 20 | 2014 |

Inverse problems with Poisson data: Tikhonov-type regularization and iteratively regularized Newton methods F Werner Der Andere Verlag, 2012 | 16 | 2012 |

Multidimensional multiscale scanning in Exponential Families: Limit theory and statistical consequences C König, A Munk, F Werner The Annals of Statistics 48 (2), 655-678, 2020 | 12 | 2020 |

On convergence rates for iteratively regularized Newton-type methods under a Lipschitz-type nonlinearity condition F Werner Journal of Inverse and Ill-Posed Problems 23 (1), 75-84, 2015 | 12 | 2015 |

Bump detection in heterogeneous Gaussian regression F Enikeeva, A Munk, F Werner Bernoulli 24 (2), 1266-1306, 2018 | 10 | 2018 |

Convergence Analysis of (Statistical) Inverse Problems under Conditional Stability Estimates F Werner, B Hofmann Inverse Problems 36 (1), 015004, 2020 | 8 | 2020 |

Empirical Risk Minimization as Parameter Choice Rule for General Linear Regularization Methods H Li, F Werner Annales de l’Institut Henri Poincaré 56 (1), 405-427, 2020 | 8 | 2020 |

Adaptivity and Oracle Inequalities in Linear Statistical Inverse Problems: A (Numerical) Survey F Werner New Trends in Parameter Identification for Mathematical Models, 291-316, 2018 | 5 | 2018 |

Statistical foundations of nanoscale photonic imaging A Munk, T Staudt, F Werner Nanoscale Photonic Imaging, 125-143, 2020 | 3 | 2020 |

Bump detection in the presence of dependency: Does it ease or does it load? F Enikeeva, A Munk, M Pohlmann, F Werner Bernoulli 26 (4), 3280-3310, 2020 | 3 | 2020 |

Photonic imaging with statistical guarantees: From multiscale testing to multiscale estimation A Munk, K Proksch, H Li, F Werner Nanoscale Photonic Imaging, 283-312, 2020 | 1 | 2020 |

Discussion of "Hypothesis testing by convex optimization" by A. Goldenshluger, A. Juditsky and A. Nemirovski. A Munk, F Werner Electronic Journal of Statistics 9 (2), 1720-1722, 2015 | 1 | 2015 |

On the asymptotical regularization for linear inverse problems in presence of white noise S Lu, P Niu, F Werner SIAM/ASA Journal on Uncertainty Quantification 9 (1), 1–28, 2021 | | 2021 |

What is resolution? A statistical minimax testing perspective on super-resolution microscopy G Kulaitis, A Munk, F Werner Accepted for The Annals of Statistics, 2020 | | 2020 |

Variational multiscale nonparametric regression: Algorithms M del Alamo, H Li, A Munk, F Werner Algorithms 13 (11), 296, 2020 | | 2020 |