S.np: Smoothing matrix by nonparametric methods

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Provides the smoothing matrix S for the discretization points tt

Usage

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S.LLR(tt, h, Ker = Ker.norm, w = NULL, cv = FALSE)

S.LPR(tt, h, p = 1, Ker = Ker.norm, w = NULL, cv = FALSE)

S.LCR(tt, h, Ker = Ker.norm, w = NULL, cv = FALSE)

S.NW(tt, h = NULL, Ker = Ker.norm, w = NULL, cv = FALSE)

S.KNN(tt, h = NULL, Ker = Ker.unif, w = NULL, cv = FALSE)

Arguments

tt

Vector of discretization points or distance matrix mdist

h

Smoothing parameter or bandwidth. In S.KNN, number of k-nearest neighbors.

Ker

Type of kernel used, by default normal kernel.

w

Optional case weights.

cv

If TRUE, cross-validation is done.

p

Polynomial degree. be passed by default to create.basis

Details

Options:

Value

Return the smoothing matrix S.

Author(s)

Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es

References

Ferraty, F. and Vieu, P. (2006). Nonparametric functional data analysis. Springer Series in Statistics, New York.

Wasserman, L. All of Nonparametric Statistics. Springer Texts in Statistics, 2006.

Opsomer, J. D., and Ruppert, D. (1997). Fitting a bivariate additive model by local polynomial regression. The Annals of Statistics, 25(1), 186-211.

See Also

See Also as S.basis

Examples

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## Not run: 
  tt=1:101
  S=S.LLR(tt,h=5)
  S2=S.LLR(tt,h=10,Ker=Ker.tri)
  S3=S.NW(tt,h=10,Ker=Ker.tri)
  S4=S.KNN(tt,h=5,Ker=Ker.tri)
  par(mfrow=c(2,3))
  image(S)
  image(S2)
  image(S3)
  image(S4)
  S5=S.LPR(tt,h=10,p=1, Ker=Ker.tri)
  S6=S.LCR(tt,h=10,Ker=Ker.tri)
  image(S5)
  image(S6)

## End(Not run)

fda.usc documentation built on Feb. 18, 2020, 1:07 a.m.