@article{BaringhausGaigall2019,
  author    = {Baringhaus, Ludwig and Gaigall, Daniel},
  title     = {On an asymptotic relative efficiency concept based on expected volumes of confidence regions},
  series = {Statistics - A Journal of Theoretical and Applied Statistic},
  volume    = {53},
  journal   = {Statistics - A Journal of Theoretical and Applied Statistic},
  number    = {6},
  publisher = {Taylor \& Francis},
  address   = {London},
  issn      = {1029-4910},
  doi       = {10.1080/02331888.2019.1683560},
  pages     = {1396 -- 1436},
  year      = {2019},
  abstract  = {The paper deals with an asymptotic relative efficiency concept for confidence regions of multidimensional parameters that is based on the expected volumes of the confidence regions. Under standard conditions the asymptotic relative efficiencies of confidence regions are seen to be certain powers of the ratio of the limits of the expected volumes. These limits are explicitly derived for confidence regions associated with certain plugin estimators, likelihood ratio tests and Wald tests. Under regularity conditions, the asymptotic relative efficiency of each of these procedures with respect to each one of its competitors is equal to 1. The results are applied to multivariate normal distributions and multinomial distributions in a fairly general setting.},
  language  = {en}
}
@article{BaringhausGaigallThiele2018,
  author    = {Baringhaus, Ludwig and Gaigall, Daniel and Thiele, Jan Philipp},
  title     = {Statistical inference for L²-distances to uniformity},
  series = {Computational Statistics},
  volume    = {2018},
  journal   = {Computational Statistics},
  number    = {33},
  publisher = {Springer},
  address   = {Berlin},
  issn      = {1613-9658},
  doi       = {10.1007/s00180-018-0820-0},
  pages     = {1863 -- 1896},
  year      = {2018},
  abstract  = {The paper deals with the asymptotic behaviour of estimators, statistical tests and confidence intervals for L²-distances to uniformity based on the empirical distribution function, the integrated empirical distribution function and the integrated empirical survival function. Approximations of power functions, confidence intervals for the L²-distances and statistical neighbourhood-of-uniformity validation tests are obtained as main applications. The finite sample behaviour of the procedures is illustrated by a simulation study.},
  language  = {en}
}