LSfunctional: Compute least squares criterion for two ordered isotonic...

In OrdMonReg: Compute least squares estimates of one bounded or two ordered isotonic regression curves

Description

Computes the value of the least squares criterion in the problem of two ordered isotonic regression functions.

Usage

LSfunctional(f1, g1, w1, f2, g2, w2)

Arguments

f1	Vector in R^n, specifies values of upper function at which criterion should be evaluated.
g1	Vector in R^n, measurements of upper function.
w1	Vector in R^n, weights for upper function.
f2	Vector in R^n, specifies values of lower function at which criterion should be evaluated.
g2	Vector in R^n, measurements of lower function.
w2	Vector in R^n, weights for lower function.

Details

This function simply computes for the above vectors

L(f1, f2) \ = \ ∑_{i=1}^n w1_i(f1_i - g1_i)^2 + ∑_{i=1}^n w2_i(f2_i - g2_i)^2.

Author(s)

Fadoua Balabdaoui [e-mail]
http://www.ceremade.dauphine.fr/~fadoua

Kaspar Rufibach (maintainer) [e-mail]
http://www.kasparrufibach.ch

Filippo Santambrogio [e-mail]
http://www.math.u-psud.fr/~santambr/

References

Balabdaoui, F., Rufibach, K., Santambrogio, F. (2009). Least squares estimation of two ordered monotone regression curves. Preprint.

See Also

This function is used by BoundedAntiMeanTwo.

OrdMonReg documentation built on May 2, 2019, 1:45 p.m.