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 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 - Gaigall, Daniel T1 - Test for Changes in the Modeled Solvency Capital Requirement of an Internal Risk Model JF - ASTIN Bulletin N2 - In the context of the Solvency II directive, the operation of an internal risk model is a possible way for risk assessment and for the determination of the solvency capital requirement of an insurance company in the European Union. A Monte Carlo procedure is customary to generate a model output. To be compliant with the directive, validation of the internal risk model is conducted on the basis of the model output. For this purpose, we suggest a new test for checking whether there is a significant change in the modeled solvency capital requirement. Asymptotic properties of the test statistic are investigated and a bootstrap approximation is justified. A simulation study investigates the performance of the test in the finite sample case and confirms the theoretical results. The internal risk model and the application of the test is illustrated in a simplified example. The method has more general usage for inference of a broad class of law-invariant and coherent risk measures on the basis of a paired sample. KW - Bootstrap KW - Empirical process KW - Functional Delta Method KW - Hadamard differentiability KW - Paired sample Y1 - 2021 U6 - http://dx.doi.org/10.1017/asb.2021.20 SN - 1783-1350 VL - 51 IS - 3 SP - 813 EP - 837 PB - Cambridge Univ. Press CY - Cambridge 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 - TY - JOUR A1 - Ditzhaus, Marc A1 - Gaigall, Daniel T1 - Testing marginal homogeneity in Hilbert spaces with applications to stock market returns JF - Test N2 - This paper considers a paired data framework and discusses the question of marginal homogeneity of bivariate high-dimensional or functional data. The related testing problem can be endowed into a more general setting for paired random variables taking values in a general Hilbert space. To address this problem, a Cramér–von-Mises type test statistic is applied and a bootstrap procedure is suggested to obtain critical values and finally a consistent test. The desired properties of a bootstrap test can be derived that are asymptotic exactness under the null hypothesis and consistency under alternatives. Simulations show the quality of the test in the finite sample case. A possible application is the comparison of two possibly dependent stock market returns based on functional data. The approach is demonstrated based on historical data for different stock market indices. Y1 - 2022 U6 - http://dx.doi.org/10.1007/s11749-022-00802-5 SN - 1863-8260 VL - 2022 IS - 31 SP - 749 EP - 770 PB - Springer ER - TY - JOUR A1 - Gaigall, Daniel A1 - Gerstenberg, Julian A1 - Trinh, Thi Thu Ha T1 - Empirical process of concomitants for partly categorial data and applications in statistics JF - Bernoulli N2 - On the basis of independent and identically distributed bivariate random vectors, where the components are categorial and continuous variables, respectively, the related concomitants, also called induced order statistic, are considered. The main theoretical result is a functional central limit theorem for the empirical process of the concomitants in a triangular array setting. A natural application is hypothesis testing. An independence test and a two-sample test are investigated in detail. The fairly general setting enables limit results under local alternatives and bootstrap samples. For the comparison with existing tests from the literature simulation studies are conducted. The empirical results obtained confirm the theoretical findings. KW - bootstrap KW - Categorial variable KW - Concomitant KW - Empirical process KW - Independence test Y1 - 2022 U6 - http://dx.doi.org/10.3150/21-BEJ1367 SN - 1573-9759 VL - 28 IS - 2 SP - 803 EP - 829 PB - International Statistical Institute CY - Den Haag, NL ER - TY - JOUR A1 - Baringhaus, Ludwig A1 - Gaigall, Daniel T1 - A goodness-of-fit test for the compound Poisson exponential model JF - Journal of Multivariate Analysis N2 - On the basis of bivariate data, assumed to be observations of independent copies of a random vector (S,N), we consider testing the hypothesis that the distribution of (S,N) belongs to the parametric class of distributions that arise with the compound Poisson exponential model. Typically, this model is used in stochastic hydrology, with N as the number of raindays, and S as total rainfall amount during a certain time period, or in actuarial science, with N as the number of losses, and S as total loss expenditure during a certain time period. The compound Poisson exponential model is characterized in the way that a specific transform associated with the distribution of (S,N) satisfies a certain differential equation. Mimicking the function part of this equation by substituting the empirical counterparts of the transform we obtain an expression the weighted integral of the square of which is used as test statistic. We deal with two variants of the latter, one of which being invariant under scale transformations of the S-part by fixed positive constants. Critical values are obtained by using a parametric bootstrap procedure. The asymptotic behavior of the tests is discussed. A simulation study demonstrates the performance of the tests in the finite sample case. The procedure is applied to rainfall data and to an actuarial dataset. A multivariate extension is also discussed. KW - Bootstrapping KW - Collective risk model Y1 - 2022 U6 - http://dx.doi.org/10.1016/j.jmva.2022.105154 SN - 0047-259X SN - 1095-7243 VL - 195 IS - Article 105154 PB - Elsevier CY - Amsterdam ER -