Institut für Mathematische Stochastik

Publikationen: Proksch

  • 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. The Annals of Statistics, arXiv:1611.04537. To appear.
  • 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. 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. 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.