Evaluate the fit for iterative bias reduction model

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Description

The function evaluates the fit for iterative bias reduction model for iteration k. This function is not intended to be used directly.

Usage

1
fittedS1lr(n,U,tUy,eigenvaluesS1,ddlmini,k,rank)

Arguments

n

The number of observations.

U

The the matrix of eigen vectors of the symmetric smoothing matrix S.

tUy

The transpose of the matrix of eigen vectors of the symmetric smoothing matrix S times the vector of observation y.

eigenvaluesS1

Vector of the eigenvalues of the symmetric smoothing matrix S.

ddlmini

The number of eigen values of S equal to 1.

k

A numeric vector which gives the number of iterations

rank

The rank of lowrank splines.

Details

see the reference for detailed explanation of computation of iterative bias reduction smoother

Value

Returns a vector containing the fit

Author(s)

Pierre-Andre Cornillon, Nicolas Hengartner and Eric Matzner-Lober

References

Cornillon, P.-A.; Hengartner, N.; Jegou, N. and Matzner-Lober, E. (2012) Iterative bias reduction: a comparative study. Statistics and Computing, 23, 777-791.

Cornillon, P.-A.; Hengartner, N. and Matzner-Lober, E. (2013) Recursive bias estimation for multivariate regression smoothers Recursive bias estimation for multivariate regression smoothers. ESAIM: Probability and Statistics, 18, 483-502.

Wood, S.N. (2003) Thin plate regression splines. J. R. Statist. Soc. B, 65, 95-114.

See Also

ibr

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