Estimated number of seriously injured road users admitted to hospital in France between 2010 and 2017, based on medico-administrative data

Autor: Anthony Zullo, Maxime Large, Emmanuelle Amoros, Jean-Louis Martin

Publikováno v: BMC Public Health, Vol 21, Iss 1, Pp 1-10 (2021)

Abstract Background In France, like in most developed countries, the number of road accident fatalities is estimated from police data. These estimates are considered to be good-quality, unlike estimates of road injuries admitted to hospital, and espe

Externí odkaz: https://doaj.org/article/75ae5b4be59f46b0a9400ba5da72713a

Nonparametric regression on contaminated functional predictor with application to hyperspectral data

Autor: Anthony Zullo, Frédéric Ferraty, Mathieu Fauvel

Publikováno v: Econometrics and Statistics
Econometrics and Statistics, Elsevier, 2019, 9, pp.95-107. ⟨10.1016/j.ecosta.2017.02.004⟩
Econometrics and Statistics, 2019, 9, pp.95-107. ⟨10.1016/j.ecosta.2017.02.004⟩

Regressing nonparametrically a scalar response on a contaminated random curve observed at some measurement grid may be a hard task. To address this common statistical situation, a kernel presmoothing step is achieved on the noisy functional predictor

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b1f8a7b4765eb993f0a49660a883fabe
https://hal.archives-ouvertes.fr/hal-01605940

Nonlinear parsimonious feature selection for the classification of hyperspectral images

Autor: Anthony Zullo, Frédéric Ferraty, Mathieu Fauvel

Publikováno v: 6. Workshop on Hyperspectral image and signal processing: evolution in remote sensing
6. Workshop on Hyperspectral image and signal processing: evolution in remote sensing, Jun 2014, Lausanne, Switzerland. 4 p
WHISPERS

International audience; A nonlinear parsimonious feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM). GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectral feat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bbd5be6de32f36509d3366c510b3f3eb
https://hal.inrae.fr/hal-02797359

Non-parametric functional methods for hyperspectral image classification

Autor: Frédéric Ferraty, Philippe Vieu, Anthony Zullo, Michel Goulard, Mathieu Fauvel

Publikováno v: IEEE International Geoscience and Remote Sensing Symposium Proceedings
IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul 2014, Quebec, Canada. pp.4, ⟨10.1109/IGARSS.2014.6947217⟩
IGARSS

International audience; The objective of this article is to assess the relevance of a statistical method for hyperspectral image classification. We focus on the implementation of a functional method whose main objective is to consider each hyperspect

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::142997ac49f5a0aab96d59d9efc9a490
https://hal.inrae.fr/hal-02740723