Change-point estimation in the multivariate model taking into account the dependence: Application to the vegetative development of oilseed rape - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of Agricultural, Biological, and Environmental Statistics Année : 2018

Change-point estimation in the multivariate model taking into account the dependence: Application to the vegetative development of oilseed rape

Résumé

In this paper, we address the change-point estimation issue in multivariate observations which consist in functions having piecewise constant first derivatives corrupted by some additional noise. We propose to solve this problem by rewriting it as a variable selection issue in a sparse multivariate linear model. Moreover, the methodology that we propose takes into account the dependence that may exist within the multivariate observations. Then, the performance of our approach is assessed through some numerical experiments and compared to other alternative and classical methods. Finally, we apply our methodology to experimental data in order to study the vegetative development of oilseed rape. The evolution of the number of leaves of oilseed rape can be modeled as a function having piecewise constant first derivatives corrupted by some additional noise where the change-points correspond to key times in the plant phenology. Our novel estimation method increases the accuracy of the change-point estimation in comparison with classical approaches. Moreover, we show that the parameters of the covariance matrix depend on the level of competition between plants.
Fichier principal
Vignette du fichier
brault_levy_mathieu_jullien_JABES_HAL (1).pdf (452.28 Ko) Télécharger le fichier
Loading...

Dates et versions

hal-01809633 , version 1 (08-06-2018)

Identifiants

Citer

Vincent Brault, C. Lévy-Leduc, Amélie Mathieu, Alexandra Jullien. Change-point estimation in the multivariate model taking into account the dependence: Application to the vegetative development of oilseed rape. Journal of Agricultural, Biological, and Environmental Statistics, 2018, 23 (3), pp.374-389. ⟨10.1007/s13253-018-0324-y⟩. ⟨hal-01809633⟩
200 Consultations
179 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More