Rearrangement.estimation {LorenzRegression} | R Documentation |
Estimates a monotonic regression curve via Chernozhukov et al (2009)
Description
Rearrangement.estimation
estimates the increasing link function of a single index model via the methodology proposed in Chernozhukov et al (2009).
Usage
Rearrangement.estimation(Y, Index, t = Index, weights = NULL, degree.pol = 1)
Arguments
Y |
The response variable. |
Index |
The estimated index. The user may obtain it using function |
t |
A vector of points over which the link function |
weights |
vector of sample weights. By default, each observation is given the same weight. |
degree.pol |
degree of the polynomial used in the local polynomial regression. Default value is 1. |
Details
A first estimator of the link function, neglecting the assumption of monotonicity, is obtained with function locpol
from the locpol package.
The final estimator is obtained through the rearrangement operation explained in Chernozhukov et al (2009). This operation is carried out with function rearrangement
from package Rearrangement
.
Value
A list with the following components
t
the points over which the estimation has been undertaken.
H
the estimated link function evaluated at t.
References
Chernozhukov, V., I. Fernández-Val, and A. Galichon (2009). Improving Point and Interval Estimators of Monotone Functions by Rearrangement. Biometrika 96 (3). 559–75.
See Also
Lorenz.Reg
, locpol
, rearrangement
Examples
data(Data.Incomes)
PLR <- Lorenz.Reg(Income ~ ., data = Data.Incomes, penalty = "SCAD",
h.grid = nrow(Data.Incomes)^(-1/5.5), eps = 0.01)
Y <- PLR$Fit[,1]
Index <- PLR$Fit[,2]
Rearrangement.estimation(Y = Y, Index = Index)