I am a mathematician with main research interests in non-parametric and asymptotic statistics, as well as statistical inverse problems. Of particular interest to me is the development and theoretical investigation of means of statistical inference for applications from the natural sciences. In this context, I have been working on the construction of statistical tests and confidence statements with a recent focus on statistical multiscale analysis and its application to (nanophotonic) imaging and inference on three-dimensional molecular distributions in biological samples. Most of my scientific work has been at the interface of several disciplines, which is what I really love about statistics.
# Estimator # Ill-Posed Problem # Inverse Model # Inverse Regression # Radon Transform # Regression Function # Regularization Parameter # Simultaneous Inference
Proksch, K., Weitkamp, C. A., Staudt, T., Lelandais, B., & Zimmer, C. (2022). From Small Scales to Large Scales: Distance-to-Measure Density based Geometric Analysis of Complex Data. https://arxiv.org/abs/2205.07689
Finocchio, G. (2021). Two perspectives on high-dimensional estimation problems: posterior contraction and median-of-means. University of Twente. https://doi.org/10.3990/1.9789036552356
Bissantz, K., Bissantz, N. , & Proksch, K. (2021). Nonparametric detection of changes over time in image data from fluorescence microscopy of living cells. Scandinavian journal of statistics, 48(3), 1001-1017. https://doi.org/10.1111/sjos.12517
Finocchio, G. , Derumigny, A. , & Proksch, K. (2021). Robust-to-outliers square-root LASSO, simultaneous inference with a MOM approach. arXiv.org.
Munk, A. , Proksch, K., Li, H., & Werner, F. (2020). Photonic imaging with statistical guarantees: From multiscale testing to multiscale estimation. In Topics in Applied Physics (pp. 283-312). (Topics in Applied Physics; Vol. 134). Springer. https://doi.org/10.1007/978-3-030-34413-9_11
Proksch, K., Bissantz, N., & Holzmann, H. (2020). Simultaneous inference for Berkson errors-in-variables regression under fixed design. arXiv.org. https://arxiv.org/abs/2009.00936
Weitkamp, C. A. , Proksch, K., Tameling, C., & Munk, A. (2020). Gromov-Wasserstein Distance based Object Matching: Asymptotic Inference. arXiv.org. https://arxiv.org/abs/2006.12287
Dunker, F., Eckle, K. , Proksch, K. , & Schmidt-Hieber, A. J. (2019). Tests for qualitative features in the random coefficients model. Electronic Journal of Statistics, 13(2), 2257-2306. https://doi.org/10.1214/19-EJS1570
Eckle, K., Bissantz, N., Dette, H. , Proksch, K., & Einecke, S. (2018). Multiscale inference for a multivariate density with applications to X-ray astronomy. Annals of the Institute of Statistical Mathematics, 70(3), 647-689. https://doi.org/10.1007/s10463-017-0605-1
Röken, C., Schuppan, F. , Proksch, K., & Schöneberg, S. (2018). Flaring of blazars from an analytical, time-dependent model for combined synchrotron and synchrotron self-Compton radiative losses of multiple ultrarelativistic electron populations. Astronomy & astrophysics, 616(august), [A172]. https://doi.org/10.1051/0004-6361/201730622
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Courses Academic Year 2022/2023
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