update fpca references

docs/refs.bib

@@ -493,6 +493,25 @@ @inbook{ramsay+silverman_2005_registration
   keywords = {Multivariate analysis}
 }
 
+@inbook{ramsay+silverman_2005_basisfuncexp,
+  title = {Basis function expansion of the functions},
+  booktitle = {Functional Data Analysis},
+  author = {Ramsay, James and Silverman, Bernard W.},
+  year = {2005},
+  series = {Springer {{Series}} in {{Statistics}}},
+  edition = {Second},
+  pages = {127--145},
+  publisher = {{Springer-Verlag}},
+  address = {{New York}},
+  doi = {10.1007/b98888},
+  url = {https://www.springer.com/gp/book/9780387400808},
+  urldate = {2024-10-20},
+  collaborator = {Ramsay, James and Silverman, Bernard W.},
+  isbn = {978-0-387-40080-8},
+  langid = {english},
+  keywords = {Multivariate analysis}
+}
+
 @incollection{romeo+marzoljaen_2014_analisis,
   title = {{An\'alisis del viento y la niebla en el aeropuerto de Los Rodeos (Tenerife). Cambios y tendencias}},
   booktitle = {{Cambio clim\'atico y cambio global.}},

skfda/preprocessing/dim_reduction/_fpca.py

@@ -29,8 +29,10 @@ class FPCA(  # noqa: WPS230 (too many public attributes)
 
     Class that implements functional principal component analysis for both
     basis and grid representations of the data. The parameters are shared
-    when fitting a FDataBasis or FDataGrid, except for
-    ``components_basis``.
+    when fitting a FDataBasis or FDataGrid, except for ``components_basis``.
+
+    For more information about the implementation of the computation of the
+    first principal components see :footcite:ts:`silverman_2005_basisfuncexp`.
 
     Parameters:
         n_components: Number of principal components to keep from
@@ -88,6 +90,10 @@ class FPCA(  # noqa: WPS230 (too many public attributes)
         >>> fd = FDataGrid(data_matrix, grid_points)
         >>> fpca_grid = FPCA(2)
         >>> fpca_grid = fpca_grid.fit(fd)
+
+    References:
+        .. footbibliography::
+
     """
 
     def __init__(
@@ -137,7 +143,7 @@ def _fit_basis(
 
         The eigenvalues associated with these principal components are also
         saved. For more details about how it is implemented please view the
-        referenced book.
+        book referenced in the class docstring.
 
         Args:
             X: The functional data object to be analysed.
@@ -146,11 +152,6 @@ def _fit_basis(
         Returns:
             self
 
-        References:
-            .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function
-                expansion of the functions. In *Functional Data Analysis*
-                (pp. 161-164). Springer.
-
         """
         # the maximum number of components is established by the target basis
         # if the target basis is available.