An implementation of the procedures in Zhong et al. (2019) and Santo and Zhong (2020) for testing the homogeneity of covariance matrices, and estimating multiple change-points in high-dimensional (Hi-Dim) longitudinal/functional data with general temporospatial dependence. The null hypothesis of the homogeneity test is that all covariance matrices are equal at each time point. If the null hypothesis is rejected, the procedure further identifies the locations of the change points. Note: The package uses Open MP. Mac OS X users may need to update clang compiler so that it supports Open MP.
test_covmat
cpi_covmat
| Package: | TechPhD |
| Type: | package |
| Version: | 1.0.0 |
| Date: | 2020-04-06 |
| License: | GPL-2 |
Zhong, Li, and Santo (2019). Homogeneity tests of covariance matrices with high-dimensional longitudinal data. Biometrika, 106, 619-634
Santo and Zhong (2020). Homogeneity tests of covariance and change-points identification for high-dimensional functional data. arXiv:2005.01895