Journal of Multivariate Analysis
Depth estimators and tests based on the likelihood principle with application to regression
Journal of Multivariate Analysis
Calculation of simplicial depth estimators for polynomial regression with applications
Computational Statistics & Data Analysis
Measuring overlap in binary regression
Computational Statistics & Data Analysis
Tests for multiple regression based on simplicial depth
Journal of Multivariate Analysis
Depth notions for orthogonal regression
Journal of Multivariate Analysis
| Hi-index | 0.00 |
A general approach for developing distribution-free tests for general linear models based on simplicial depth is presented. In most relevant cases, the test statistic is a degenerated U-statistic so that the spectral decomposition of the conditional expectation of the kernel function is needed to derive the asymptotic distribution. A general formula for this conditional expectation is derived. Then it is shown how this general formula can be specified for polynomial regression. Based on the specified form, the spectral decomposition and thus the asymptotic distribution is derived for polynomial regression of arbitrary degree. The power of the new test is compared via simulation with other tests. An application on cubic regression demonstrates the applicability of the new tests and in particular their outlier robustness.