Abstract: This talk focuses on the problems of i) detecting change-points near the tails of time series in a univariate setting and ii) domination between marginals when performing multivariate analysis. An adaptive sliding-window-based approach is proposed for the former case, and a hierarchical approach is developed for the latter. Both approaches are studied via comprehensive simulation studies showing that the proposed approaches outperform the state-of-the-art in various senses. Applications to NDVI, LST, and point processes are considered.
This talk is based on the following publications:
[1] Moradi, M., Montesino-SanMartin, M., Ugarte, M. D., & Militino, A. F. (2022). Locally adaptive change-point detection (LACPD) with applications to environmental changes. Stochastic Environmental Research and Risk Assessment, 36(1), 251-269.
[2] Moradi, M., Cronie, O., Pérez-Goya, U., & Mateu, J. (2023). Hierarchical spatio-temporal change-point detection. The American Statistician, 1-11.
