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 - https://doi.org/10.1016/j.jmva.2022.105154 SN - 0047-259X SN - 1095-7243 VL - 195 IS - Article 105154 PB - Elsevier CY - Amsterdam ER - TY - THES A1 - Gaigall, Daniel T1 - On selected problems in multivariate analysis N2 - Selected problems in the field of multivariate statistical analysis are treated. Thereby, one focus is on the paired sample case. Among other things, statistical testing problems of marginal homogeneity are under consideration. In detail, properties of Hotelling‘s T² test in a special parametric situation are obtained. Moreover, the nonparametric problem of marginal homogeneity is discussed on the basis of possibly incomplete data. In the bivariate data case, properties of the Hoeffding-Blum-Kiefer-Rosenblatt independence test statistic on the basis of partly not identically distributed data are investigated. Similar testing problems are treated within the scope of the application of a result for the empirical process of the concomitants for partly categorial data. Furthermore, testing changes in the modeled solvency capital requirement of an insurance company by means of a paired sample from an internal risk model is discussed. Beyond the paired sample case, a new asymptotic relative efficiency concept based on the expected volumes of multidimensional confidence regions is introduced. Besides, a new approach for the treatment of the multi-sample goodness-of-fit problem is presented. Finally, a consistent test for the treatment of the goodness-of-fit problem is developed for the background of huge or infinite dimensional data. KW - Paired sample KW - Marginal homogeneity KW - Incomplete data KW - Asymptotic relative efficiency KW - Volumes of confidence regions Y1 - 2023 U6 - https://doi.org/10.15488/14304 N1 - Gottfried Wilhelm Leibniz Universität Hannover ER - TY - JOUR A1 - Gaigall, Daniel ED - AitSahlia, Farid T1 - Allocating and forecasting changes in risk JF - Journal of risk N2 - We consider time-dependent portfolios and discuss the allocation of changes in the risk of a portfolio to changes in the portfolio’s components. For this purpose we adopt established allocation principles. We also use our approach to obtain forecasts for changes in the risk of the portfolio’s components. To put the approach into practice we present an implementation based on the output of a simulation. Allocation is illustrated with an example portfolio in the context of Solvency II. The quality of the forecasts is investigated with an empirical study. KW - portfolio risk KW - allocation KW - forecast KW - covariance principle KW - conditional expectation principle Y1 - 2023 U6 - https://doi.org/10.21314/JOR.2022.048 SN - 1755-2842 SN - 1465-1211 VL - 25 IS - 3 SP - 1 EP - 24 PB - Infopro Digital Risk CY - London ER - TY - JOUR A1 - Gaigall, Daniel T1 - On the applicability of several tests to models with not identically distributed random effects JF - Statistics : A Journal of Theoretical and Applied Statistics N2 - We consider Kolmogorov–Smirnov and Cramér–von-Mises type tests for testing central symmetry, exchangeability, and independence. In the standard case, the tests are intended for the application to independent and identically distributed data with unknown distribution. The tests are available for multivariate data and bootstrap procedures are suitable to obtain critical values. We discuss the applicability of the tests to random effects models, where the random effects are independent but not necessarily identically distributed and with possibly unknown distributions. Theoretical results show the adequacy of the tests in this situation. The quality of the tests in models with random effects is investigated by simulations. Empirical results obtained confirm the theoretical findings. A real data example illustrates the application. KW - central symmetry test KW - exchangeability test KW - independence test KW - random effects KW - not identically distributed Y1 - 2023 SN - 0323-3944 U6 - https://doi.org/10.1080/02331888.2023.2193748 SN - 1029-4910 VL - 57 PB - Taylor & Francis CY - London ER - TY - JOUR A1 - Gaigall, Daniel A1 - Gerstenberg, Julian T1 - Cramér-von-Mises tests for the distribution of the excess over a confidence level JF - Journal of Nonparametric Statistics N2 - The Cramér-von-Mises distance is applied to the distribution of the excess over a confidence level. Asymptotics of related statistics are investigated, and it is seen that the obtained limit distributions differ from the classical ones. For that reason, quantiles of the new limit distributions are given and new bootstrap techniques for approximation purposes are introduced and justified. The results motivate new one-sample goodness-of-fit tests for the distribution of the excess over a confidence level and a new confidence interval for the related fitting error. Simulation studies investigate size and power of the tests as well as coverage probabilities of the confidence interval in the finite sample case. A practice-oriented application of the Cramér-von-Mises tests is the determination of an appropriate confidence level for the fitting approach. The adoption of the idea to the well-known problem of threshold detection in the context of peaks over threshold modelling is sketched and illustrated by data examples. KW - Cramér-von-Mises test KW - conditional excess distribution KW - confidence interval KW - goodness-of-fit test Y1 - 2023 U6 - https://doi.org/10.1080/10485252.2023.2173958 SN - 1048-5252 (Print) SN - 1029-0311 (Online) PB - Taylor & Francis 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 - https://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 - 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 - https://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 - 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 - https://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 - 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 - https://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 - 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 - https://doi.org/10.1080/03610918.2019.1618472 SN - 1532-4141 VL - 53 IS - 10 SP - 2971 EP - 2989 PB - Taylor & Francis CY - London ER -