- Proksch, K., Werner, F., Keller-Findeisen, J., Ta, H., Munk, A. (2022).
Towards quantitative super-resolution microscopy: Molecular maps with statistical guarantees. arXiv:2207.13426. Submitted. - Weitkamp, CA., Proksch, K., Tameling, C., Munk, A. (2020).
Gromov-Wasserstein Distance based Object Matching: Asymptotic Inference. arXiv:2006.12287. Submitted. - Röken, C., Schuppan, F., Proksch, K. and 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.
DOI:10.1051/0004-6361/201730622, Astronomy & Astrophysics. To appear. - Proksch, K., Werner, F., Munk, A. (2018).
Multiscale scanning in inverse problems. arXiv:1611.04537 The Annals of Statistics, 46 (6B), 3569-3602. - Bissantz, K., Bissantz, N. and Proksch, K. (2017).
Nonparametric detection of changes over time in image data
from fluorescence microscopy of living cells.
Submitted. - Chao, S.-K., Proksch, K., Dette, H. and Härdle, W. K. (2017).
Confidence corridors for multivariate generalized quantile
regression. Journal of Business & Economic Statistics, 35, 70-85. - Eckle, K., Bissantz, N., Dette, H., Proksch, K.
Einecke, S. (2017).
Multiscale inference for a multivariate density with applications to X-ray astronomy.
DOI:10.1007/s10463-017-0605-1, Annals of the Institute of Statistical Mathematics. To appear. - Dunker, F., Eckle, K., Proksch, K. and Schmidt-Hieber, J. (2017).
Tests for Qualitative Features in the Random Coefficients Model. arxiv.org/abs/1704.01066. Submitted. - Lucka, F., Proksch, K., Brune, C., Bissantz, N., Burger, M., Dette, H. and Wübbeling, F. (2017).
Risk Estimators for Choosing Regularization Parameters in Ill-Posed Problems - Properties and Limitations. arxiv.org/abs/1701.04970. Submitted. - Einecke, S., Proksch, K., Bissantz, N., Clevermann, F. and Rhode, W. (2016).
Uncertainty Limits on Solutions of Inverse Problems over Multiple Orders of Magnitude using Bootstrap Methods: An Astroparticle Physics Example. arXiv:1607.07226. Submitted. - Proksch, K. (2016).
On confidence bands for multivariate nonparametric regression. Annals of the Institute of Statistical Mathematics, 68, 209-236. - Proksch, K., Bissantz, N. and Dette, H. (2015).
Confidence bands for multivariate and time dependent inverse
regression models. Bernoulli, 21, 144-175. - Bissantz, N., Holzmann, H. and Proksch, K. (2014).
Confidence regions for images observed under the Radon
transform. Journal of Multivariate Analysis, 128, 86-107. - Proksch, K. (2013).
Asymptotic Normality of Kernel Estimators for Images
Observed under the Radon Transform in Fan Beam Design.
AIP Conf. Proc., 1558, 728-731 Submitted. - Proksch, K., Bissantz, N. and Dette, H. (2012).
A note on asymptotic uniform confidence bands in a multivariate statistical deconvolution problem. AIP Conf. Proc., 1479, 438-441. - Bissantz, N., Dette, H. and Proksch, K. (2012).
Model checks in inverse regression models with
convolution-type operators Scandinavian Journal of Statistics., 39, 305-322. - Proksch, K., Bissantz, N. and Dette, H. (2011).
On Bootstrapping L^2-Type Statistics in Inverse Regression Models with Convolution-Type Operators. AIP Conf. Proc., 1389, 414-418.
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