@article{Gaigall2020,
  author    = {Gaigall, Daniel},
  title     = {Testing marginal homogeneity of a continuous bivariate distribution with possibly incomplete paired data},
  series = {Metrika},
  volume    = {2020},
  journal   = {Metrika},
  number    = {83},
  publisher = {Springer},
  issn      = {1435-926X},
  doi       = {10.1007/s00184-019-00742-5},
  pages     = {437 -- 465},
  year      = {2020},
  abstract  = {We discuss the testing problem of homogeneity of the marginal distributions of a continuous bivariate distribution based on a paired sample with possibly missing components (missing completely at random). Applying the well-known two-sample Cr{\´a}mer-von-Mises distance to the remaining data, we determine the limiting null distribution of our test statistic in this situation. It is seen that a new resampling approach is appropriate for the approximation of the unknown null distribution. We prove that the resulting test asymptotically reaches the significance level and is consistent. Properties of the test under local alternatives are pointed out as well. Simulations investigate the quality of the approximation and the power of the new approach in the finite sample case. As an illustration we apply the test to real data sets.},
  language  = {en}
}
@article{Gaigall2021,
  author    = {Gaigall, Daniel},
  title     = {Test for Changes in the Modeled Solvency Capital Requirement of an Internal Risk Model},
  series = {ASTIN Bulletin},
  volume    = {51},
  journal   = {ASTIN Bulletin},
  number    = {3},
  publisher = {Cambridge Univ. Press},
  address   = {Cambridge},
  issn      = {1783-1350},
  doi       = {10.1017/asb.2021.20},
  pages     = {813 -- 837},
  year      = {2021},
  abstract  = {In the context of the Solvency II directive, the operation of an internal risk model is a possible way for risk assessment and for the determination of the solvency capital requirement of an insurance company in the European Union. A Monte Carlo procedure is customary to generate a model output. To be compliant with the directive, validation of the internal risk model is conducted on the basis of the model output. For this purpose, we suggest a new test for checking whether there is a significant change in the modeled solvency capital requirement. Asymptotic properties of the test statistic are investigated and a bootstrap approximation is justified. A simulation study investigates the performance of the test in the finite sample case and confirms the theoretical results. The internal risk model and the application of the test is illustrated in a simplified example. The method has more general usage for inference of a broad class of law-invariant and coherent risk measures on the basis of a paired sample.},
  language  = {en}
}