Evaluates the fits for the iterative bias reduction smoother, using a kernel smoother and its decomposition into a symmetric matrix and a diagonal matrix. This function is not intended to be used directly.
fittedA(n, eigenvaluesA, tPADmdemiY, DdemiPA, ddlmini, k)Returns a list of two components: fitted contains fitted values
and trace contains the trace (effective degree of freedom) of the iterated
bias reduction smoother.
The number of observations.
Vector of the eigenvalues of the symmetric matrix A.
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.
The square root of the diagonal matrix D times the eigen vectors of the symmetric matrix A.
The number of eigenvalues (numerically) equals to 1.
A scalar which gives the number of iterations.
Pierre-Andre Cornillon, Nicolas Hengartner and Eric Matzner-Lober.
See the reference for detailed explanation of A and D.
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.
Cornillon, P.-A.; Hengartner, N. and Matzner-Lober, E. (2017) Iterative Bias Reduction Multivariate Smoothing in R: The ibr Package. Journal of Statistical Software, 77, 1--26.
ibr