A new approach to the BHEP tests for multivariate normality
Journal of Multivariate Analysis
Shortcomings of generalized affine invariant skewness measures
Journal of Multivariate Analysis
Fourier methods for testing multivariate independence
Computational Statistics & Data Analysis
On the choice of the smoothing parameter for the BHEP goodness-of-fit test
Computational Statistics & Data Analysis
Generalized Cramér-von Mises goodness-of-fit tests for multivariate distributions
Computational Statistics & Data Analysis
Multivariate distributions with correlation matrices for nonlinear repeated measurements
Computational Statistics & Data Analysis
Model and distribution uncertainty in multivariate GARCH estimation: A Monte Carlo analysis
Computational Statistics & Data Analysis
Asymmetric laplace laws and modeling financial data
Mathematical and Computer Modelling: An International Journal
| Hi-index | 0.98 |
Consistent goodness-of-fit tests are proposed for symmetric and asymmetric multivariate Laplace distributions of arbitrary dimension. The test statistics are formulated following the Fourier-type approach of measuring the weighted discrepancy between the empirical and the theoretical characteristic function, and result in computationally convenient representations. For testing the symmetric Laplace distribution, and in the particular case of a Gaussian weight function, a limit value of these test statistics is obtained when this weight function approaches a Dirac delta function. Interestingly, this limit value is related to a couple of well-known measures of multivariate skewness. A Monte Carlo study is conducted in order to compare the new procedures with standard tests based on the empirical distribution function. A real data application is also included.