@book{DiktaScheer2021,
  author    = {Dikta, Gerhard and Scheer, Marsel},
  title     = {Bootstrap Methods: With Applications in R},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-73480-0},
  doi       = {10.1007/978-3-030-73480-0},
  pages     = {XVI, 256 Seiten},
  year      = {2021},
  abstract  = {This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time. The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.},
  language  = {en}
}
@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}
}