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  - http://dx.doi.org/10.1080/02331888.2023.2193748
SN  - 1029-4910
VL  - 57
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  -