@incollection{fdi:010088566,
  title = {{N}onparametric prediction for spatial dependent functional data : application to demersal coastal fish off {S}enegal},
  author = {{N}'diaye, {M}. and {D}abo-{N}iang, {S}. and {N}gom, {P}. and {T}hiam, {N}. and {F}all, {M}. and {B}rehmer, {P}atrice},
  editor = {},
  language = {{ENG}},
  abstract = {{F}isheries 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. {C}lassical multivariate statistical techniques such as multivariate spatial parametric prediction ({K}riging) models are commonly applied to evaluate and predict fish abundance. {T}his chapter discusses ways to model and predict high-dimensional oceanological data by functional data analysis for a better management of fishery resources. {I}t commences with an introduction of the regression model, which allows to define the predictor. {T}he chapter proposes a non-parametric spatial predictor of the catch per unit of effort of {S}enegalese coastal demersal fish species. {F}inally, it gives the application of demersal coastal fish off {S}enegal to spatial prediction.},
  keywords = {{SENEGAL} ; {ATLANTIQUE}},
  booktitle = {{M}athematical modeling of random and deterministic phenomena},
  numero = {},
  pages = {31--51},
  address = {{L}ondres ({GBR}) ; {H}oboken},
  publisher = {{ISTE} ; {W}iley},
  series = {},
  year = {2020},
  DOI = {10.1002/9781119706922.ch2},
  ISBN = {978-1-78630-454-4},
  URL = {https://www.documentation.ird.fr/hor/fdi:010088566},
}