iterchoiceAe: Selection of the number of iterations for iterative bias reduction smoothers

Description

Evaluates 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

iterchoiceAe(Y, K, eigenvaluesA, tPADmdemiY, DdemiPA, ddlmini,
ddlmaxi)

Arguments

The response variable.

A numeric vector which give the search grid for iterations.

eigenvaluesA

Vector of the eigenvalues of the symmetric matrix A.

tPADmdemiY

The transpose of the matrix of eigen vectors of the symmetric matrix A times the inverse of the square root of the diagonal matrix D.

DdemiPA

The square root of the diagonal matrix D times the eigen vectors of the symmetric matrix A.

ddlmini

The number of eigenvalues (numerically) which are 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

Returns the values of GCV, AIC, corrected AIC, BIC, gMDL, df 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.

Details

See the reference for detailed explanation of A and D

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.