@article{HeelDiktaBraekers2021,
  author    = {Heel, Mareike van and Dikta, Gerhard and Braekers, Roel},
  title     = {Bootstrap based goodness‑of‑fit tests for binary multivariate regression models},
  series = {Journal of the Korean Statistical Society},
  volume    = {51},
  journal   = {Journal of the Korean Statistical Society},
  publisher = {Springer Nature},
  address   = {Singapur},
  issn      = {2005-2863 (Online)},
  doi       = {10.1007/s42952-021-00142-4},
  pages     = {28 Seiten},
  year      = {2021},
  abstract  = {We consider a binary multivariate regression model where the conditional expectation of a binary variable given a higher-dimensional input variable belongs to a parametric family. Based on this, we introduce a model-based bootstrap (MBB) for higher-dimensional input variables. This test can be used to check whether a sequence of independent and identically distributed observations belongs to such a parametric family. The approach is based on the empirical residual process introduced by Stute (Ann Statist 25:613-641, 1997). In contrast to Stute and Zhu's approach (2002) Stute \& Zhu (Scandinavian J Statist 29:535-545, 2002), a transformation is not required. Thus, any problems associated with non-parametric regression estimation are avoided. As a result, the MBB method is much easier for users to implement. To illustrate the power of the MBB based tests, a small simulation study is performed. Compared to the approach of Stute \& Zhu (Scandinavian J Statist 29:535-545, 2002), the simulations indicate a slightly improved power of the MBB based method. Finally, both methods are applied to a real data set.},
  language  = {en}
}