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  -