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  - 
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  -