ibr (version 2.0-2)

iterchoiceS1e: Number of iterations selection for iterative bias reduction model

Description

Evaluate at each iteration proposed in the grid the value of different criteria: GCV, AIC, corrected AIC, BIC and gMDL (along with the ddl and sigma squared). The minimum of these criteria gives an estimate of the optimal number of iterations. This function is not intended to be used directly.

Usage

iterchoiceS1e(y, K, tUy, eigenvaluesS1, ddlmini, ddlmaxi)

Arguments

y

The response variable

K

A numeric vector which give the search grid for iterations

eigenvaluesS1

Vector of the eigenvalues 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.

ddlmini

The number of eigen values of S equal to 1.

ddlmaxi

The maximum df. No criteria are calculated beyond the number of iterations that leads to df bigger than this bound.

Value

and sigma squared for each value of the grid K. Inf are returned if the iteration leads to a smoother with a df bigger than ddlmaxi.

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.

See Also

ibr, iterchoiceS1