TY - JOUR A1 - Baringhaus, Ludwig A1 - Gaigall, Daniel T1 - On an independence test approach to the goodness-of-fit problem JF - Journal of Multivariate Analysis N2 - 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 Cramér–von Mises in the special cases where F is the family of gamma distributions or the family of inverse Gaussian distributions. KW - Goodness-of-fit test KW - Independence test KW - Parametric bootstrap KW - Vapnik–Čhervonenkis class KW - Gamma distribution Y1 - 2015 U6 - http://dx.doi.org/10.1016/j.jmva.2015.05.013 SN - 0047-259X VL - 2015 IS - 140 SP - 193 EP - 208 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Baringhaus, Ludwig A1 - Gaigall, Daniel T1 - On Hotelling’s T² test in a special paired sample case JF - Communications in Statistics - Theory and Methods N2 - 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. KW - complete block symmetry KW - Hotelling’s T² test KW - likelihood ratio test KW - uniformly most powerful invariant test Y1 - 2017 U6 - http://dx.doi.org/10.1080/03610926.2017.1408828 SN - 1532-415X VL - 48 IS - 2 SP - 257 EP - 267 PB - Taylor & Francis CY - London ER - TY - JOUR A1 - Baringhaus, Ludwig A1 - Gaigall, Daniel T1 - Hotelling’s T² tests in paired and independent survey samples: An efficiency comparison JF - Journal of Multivariate Analysis N2 - 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. Y1 - 2017 U6 - http://dx.doi.org/10.1016/j.jmva.2016.11.004 SN - 0047-259X VL - 2017 IS - 154 SP - 177 EP - 198 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Ditzhaus, Marc A1 - Gaigall, Daniel T1 - A consistent goodness-of-fit test for huge dimensional and functional data JF - Journal of Nonparametric Statistics N2 - 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 Cramér-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. KW - Cramér-von-Mises statistic KW - separable Hilbert space KW - huge dimensional data KW - functional data Y1 - 2018 U6 - http://dx.doi.org/10.1080/10485252.2018.1486402 SN - 1029-0311 VL - 30 IS - 4 SP - 834 EP - 859 PB - Taylor & Francis CY - Abingdon ER - TY - JOUR A1 - Baringhaus, Ludwig A1 - Gaigall, Daniel A1 - Thiele, Jan Philipp T1 - Statistical inference for L²-distances to uniformity JF - Computational Statistics N2 - 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. KW - Integrated empirical distribution (survival) function KW - Goodness-of-fit tests for uniformity KW - Numerical inversion of Laplace transforms KW - Coverage probability KW - Equivalence test Y1 - 2018 U6 - http://dx.doi.org/10.1007/s00180-018-0820-0 SN - 1613-9658 VL - 2018 IS - 33 SP - 1863 EP - 1896 PB - Springer CY - Berlin ER - TY - JOUR A1 - Baringhaus, Ludwig A1 - Gaigall, Daniel T1 - On an asymptotic relative efficiency concept based on expected volumes of confidence regions JF - Statistics - A Journal of Theoretical and Applied Statistic N2 - 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. KW - Volume of confidence regions KW - asymptotic relative efficiency KW - likelihood ratio test KW - multivariate normal distribution KW - multinomial distribution Y1 - 2019 U6 - http://dx.doi.org/10.1080/02331888.2019.1683560 SN - 1029-4910 VL - 53 IS - 6 SP - 1396 EP - 1436 PB - Taylor & Francis CY - London ER - TY - JOUR A1 - Gaigall, Daniel T1 - On a new approach to the multi-sample goodness-of-fit problem JF - Communications in Statistics - Simulation and Computation N2 - 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. KW - Goodness-of-fit test KW - Multi-sample problem KW - Parametric bootstrap Y1 - 2019 U6 - http://dx.doi.org/10.1080/03610918.2019.1618472 SN - 1532-4141 VL - 53 IS - 10 SP - 2971 EP - 2989 PB - Taylor & Francis CY - London ER - TY - JOUR A1 - Gaigall, Daniel T1 - Hoeffding-Blum-Kiefer-Rosenblatt independence test statistic on partly not identically distributed data JF - Communications in Statistics - Theory and Methods N2 - The established Hoeffding-Blum-Kiefer-Rosenblatt independence test statistic is investigated for partly not identically distributed data. Surprisingly, it turns out that the statistic has the well-known distribution-free limiting null distribution of the classical criterion under standard regularity conditions. An application is testing goodness-of-fit for the regression function in a non parametric random effects meta-regression model, where the consistency is obtained as well. Simulations investigate size and power of the approach for small and moderate sample sizes. A real data example based on clinical trials illustrates how the test can be used in applications. KW - Brownian Pillow KW - Hoeffding-Blum-Kiefer-Rosenblatt independence test KW - not identically distributed KW - random effects meta-regression model Y1 - 2020 U6 - http://dx.doi.org/10.1080/03610926.2020.1805767 SN - 1532-415X VL - 51 IS - 12 SP - 4006 EP - 4028 PB - Taylor & Francis CY - London ER - TY - JOUR A1 - Gaigall, Daniel T1 - Testing marginal homogeneity of a continuous bivariate distribution with possibly incomplete paired data JF - Metrika N2 - 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 Crámer–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. KW - Marginal homogeneity test KW - Crámer–von-Mises distance KW - Paired sample KW - Incomplete data KW - Resampling test Y1 - 2019 U6 - http://dx.doi.org/10.1007/s00184-019-00742-5 SN - 1435-926X VL - 2020 IS - 83 SP - 437 EP - 465 PB - Springer ER - TY - JOUR A1 - Gaigall, Daniel T1 - Rothman–Woodroofe symmetry test statistic revisited JF - Computational Statistics & Data Analysis N2 - The Rothman–Woodroofe symmetry test statistic is revisited on the basis of independent but not necessarily identically distributed random variables. The distribution-freeness if the underlying distributions are all symmetric and continuous is obtained. The results are applied for testing symmetry in a meta-analysis random effects model. The consistency of the procedure is discussed in this situation as well. A comparison with an alternative proposal from the literature is conducted via simulations. Real data are analyzed to demonstrate how the new approach works in practice. Y1 - 2020 U6 - http://dx.doi.org/10.1016/j.csda.2019.106837 SN - 0167-9473 VL - 2020 IS - 142 SP - Artikel 106837 PB - Elsevier CY - Amsterdam ER -