N'diaye M., Dabo-Niang S., Ngom P., Thiam N., Fall M., Brehmer Patrice. (2020). Nonparametric prediction for spatial dependent functional data : application to demersal coastal fish off Senegal. In :
Manou-Abi S.M. (ed.), Dabo-Niang S. (ed.), Salon J.J. (ed.). Mathematical modeling of random and deterministic phenomena. Londres (GBR) ; Hoboken : ISTE ; Wiley, 31-51. ISBN 978-1-78630-454-4.
Titre du document
Nonparametric prediction for spatial dependent functional data : application to demersal coastal fish off Senegal
Année de publication
2020
Type de document
Partie d'ouvrage
Auteurs
N'diaye M., Dabo-Niang S., Ngom P., Thiam N., Fall M., Brehmer Patrice
In
Manou-Abi S.M. (ed.), Dabo-Niang S. (ed.), Salon J.J. (ed.), Mathematical modeling of random and deterministic phenomena
Source
Londres (GBR) ; Hoboken : ISTE ; Wiley, 2020,
31-51 ISBN 978-1-78630-454-4
Fisheries research shows, in an ecosystem approach, how the environment or ecological parameters affect the variability of density or biomass of one species or a group of species of wildlife by studying data at different capture locations over a long period of time. Classical multivariate statistical techniques such as multivariate spatial parametric prediction (Kriging) models are commonly applied to evaluate and predict fish abundance. This chapter discusses ways to model and predict high-dimensional oceanological data by functional data analysis for a better management of fishery resources. It commences with an introduction of the regression model, which allows to define the predictor. The chapter proposes a non-parametric spatial predictor of the catch per unit of effort of Senegalese coastal demersal fish species. Finally, it gives the application of demersal coastal fish off Senegal to spatial prediction.
Plan de classement
Appliquées à la pêche [040INFSTA01]
;
Océanographie [126TELAPP05]
