@incollection{fdi:010093033,
  title = {{N}onparametric prediction and supervised classification for spatial dependent functional data under fixed sampling design},
  author = {{N}diaye, {M}. and {D}abo-{N}iang, {S}. and {N}gom, {P}. and {T}hiam, {N}. and {B}rehmer, {P}atrice and {E}l {V}ally, {Y}.},
  editor = {},
  language = {{ENG}},
  abstract = {{F}isheries science has been trying to identify the best way to analyze and predict fish biomass and its spatial distribution since several decades using, among others, kirigng model, co-kiriging model, {S}pecies {D}istribution {M}odeling and {J}oined {S}pecies {D}istribution {M}odeling, based on conventional statistical methods as {G}eneralized {L}inear {M}odels and {G}eneralized {A}dditive {M}odels, with contested results. {W}e consider a bio-ecological issue applying a non parametric spatial prediction based on a spatio-functional regression models, in a fixed design sampling context, as a supervised classification method when the variable of interest belongs to a predefined class set. {T}he proposed predictor takes into account the spatial fish distribution and environmental variable such as salinity and temperature. {T}he development of the method depends on two kernels to control both interactions between observations and locations. {T}he results show that this nonparametric spatial functional supervised classification method is an efficient tool applied to predict spatial distribution of demersal coastal fish off {S}enegal.},
  keywords = {{SENEGAL} ; {ATLANTIQUE}},
  booktitle = {{N}onlinear analysis, geometry and applications},
  numero = {},
  pages = {69--100},
  address = {{C}ham},
  publisher = {{B}irkhäuser},
  series = {{T}rends in {M}athematics},
  year = {2024},
  DOI = {10.1007/978-3-031-52681-7_3},
  ISBN = {978-3-031-52680-0},
  URL = {https://www.documentation.ird.fr/hor/fdi:010093033},
}