@article{Gaigall2019,
  author    = {Gaigall, Daniel},
  title     = {On a new approach to the multi-sample goodness-of-fit problem},
  series = {Communications in Statistics - Simulation and Computation},
  volume    = {53},
  journal   = {Communications in Statistics - Simulation and Computation},
  number    = {10},
  publisher = {Taylor \& Francis},
  address   = {London},
  issn      = {1532-4141},
  doi       = {10.1080/03610918.2019.1618472},
  pages     = {2971 -- 2989},
  year      = {2019},
  abstract  = {Suppose we have k samples X₁,₁,…,X₁,ₙ₁,…,Xₖ,₁,…,Xₖ,ₙₖ with different sample sizes ₙ₁,…,ₙₖ and unknown underlying distribution functions F₁,…,Fₖ as observations plus k families of distribution functions {G₁(⋅,ϑ);ϑ∈Θ},…,{Gₖ(⋅,ϑ);ϑ∈Θ}, each indexed by elements ϑ from the same parameter set Θ, we consider the new goodness-of-fit problem whether or not (F₁,…,Fₖ) belongs to the parametric family {(G₁(⋅,ϑ),…,Gₖ(⋅,ϑ));ϑ∈Θ}. New test statistics are presented and a parametric bootstrap procedure for the approximation of the unknown null distributions is discussed. Under regularity assumptions, it is proved that the approximation works asymptotically, and the limiting distributions of the test statistics in the null hypothesis case are determined. Simulation studies investigate the quality of the new approach for small and moderate sample sizes. Applications to real-data sets illustrate how the idea can be used for verifying model assumptions.},
  language  = {en}
}
@article{DitzhausGaigall2018,
  author    = {Ditzhaus, Marc and Gaigall, Daniel},
  title     = {A consistent goodness-of-fit test for huge dimensional and functional data},
  series = {Journal of Nonparametric Statistics},
  volume    = {30},
  journal   = {Journal of Nonparametric Statistics},
  number    = {4},
  publisher = {Taylor \& Francis},
  address   = {Abingdon},
  issn      = {1029-0311},
  doi       = {10.1080/10485252.2018.1486402},
  pages     = {834 -- 859},
  year      = {2018},
  abstract  = {A nonparametric goodness-of-fit test for random variables with values in a separable Hilbert space is investigated. To verify the null hypothesis that the data come from a specific distribution, an integral type test based on a Cram{\´e}r-von-Mises statistic is suggested. The convergence in distribution of the test statistic under the null hypothesis is proved and the test's consistency is concluded. Moreover, properties under local alternatives are discussed. Applications are given for data of huge but finite dimension and for functional data in infinite dimensional spaces. A general approach enables the treatment of incomplete data. In simulation studies the test competes with alternative proposals.},
  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}
}
@article{BaringhausGaigall2017,
  author    = {Baringhaus, Ludwig and Gaigall, Daniel},
  title     = {On Hotelling's T² test in a special paired sample case},
  series = {Communications in Statistics - Theory and Methods},
  volume    = {48},
  journal   = {Communications in Statistics - Theory and Methods},
  number    = {2},
  publisher = {Taylor \& Francis},
  address   = {London},
  issn      = {1532-415X},
  doi       = {10.1080/03610926.2017.1408828},
  pages     = {257 -- 267},
  year      = {2017},
  abstract  = {In a special paired sample case, Hotelling's T² test based on the differences of the paired random vectors is the likelihood ratio test for testing the hypothesis that the paired random vectors have the same mean; with respect to a special group of affine linear transformations it is the uniformly most powerful invariant test for the general alternative of a difference in mean. We present an elementary straightforward proof of this result. The likelihood ratio test for testing the hypothesis that the covariance structure is of the assumed special form is derived and discussed. Applications to real data are given.},
  language  = {en}
}
@article{BaringhausGaigall2017,
  author    = {Baringhaus, Ludwig and Gaigall, Daniel},
  title     = {Hotelling's T² tests in paired and independent survey samples: An efficiency comparison},
  series = {Journal of Multivariate Analysis},
  volume    = {2017},
  journal   = {Journal of Multivariate Analysis},
  number    = {154},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0047-259X},
  doi       = {10.1016/j.jmva.2016.11.004},
  pages     = {177 -- 198},
  year      = {2017},
  abstract  = {Hotelling's T² tests in paired and independent survey samples are compared using the traditional asymptotic efficiency concepts of Hodges-Lehmann, Bahadur and Pitman, as well as through criteria based on the volumes of corresponding confidence regions. Conditions characterizing the superiority of a procedure are given in terms of population canonical correlation type coefficients. Statistical tests for checking these conditions are developed. Test statistics based on the eigenvalues of a symmetrized sample cross-covariance matrix are suggested, as well as test statistics based on sample canonical correlation type coefficients.},
  language  = {en}
}
@article{BaringhausGaigall2015,
  author    = {Baringhaus, Ludwig and Gaigall, Daniel},
  title     = {On an independence test approach to the goodness-of-fit problem},
  series = {Journal of Multivariate Analysis},
  volume    = {2015},
  journal   = {Journal of Multivariate Analysis},
  number    = {140},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0047-259X},
  doi       = {10.1016/j.jmva.2015.05.013},
  pages     = {193 -- 208},
  year      = {2015},
  abstract  = {Let X₁,…,Xₙ be independent and identically distributed random variables with distribution F. Assuming that there are measurable functions f:R²→R and g:R²→R characterizing a family F of distributions on the Borel sets of R in the way that the random variables f(X₁,X₂),g(X₁,X₂) are independent, if and only if F∈F, we propose to treat the testing problem H:F∈F,K:F∉F by applying a consistent nonparametric independence test to the bivariate sample variables (f(Xᵢ,Xⱼ),g(Xᵢ,Xⱼ)),1⩽i,j⩽n,i≠j. A parametric bootstrap procedure needed to get critical values is shown to work. The consistency of the test is discussed. The power performance of the procedure is compared with that of the classical tests of Kolmogorov-Smirnov and Cram{\´e}r-von Mises in the special cases where F is the family of gamma distributions or the family of inverse Gaussian distributions.},
  language  = {en}
}