WRegEst {emplik} | R Documentation |
For the AFT model, this function computes the case weighted estimator of beta. Either the least squares estimator or the regression quantile estimator.
WRegEst(x, y, delta, LS=TRUE, tau=0.5)
x |
a matrix of size N by q. |
y |
a vector of length N, containing the censored responses. |
delta |
a vector (length N) of either 1's or 0's. d=1 means y is uncensored; d=0 means y is right censored. |
LS |
a logical value. If TRUE then the function will return the least squares estimator. If FALSE then the function will return the regression quantile estimator, with quantile specified by tau. |
tau |
a scalar, between 0 and 1. The quantile to be used in quantile regression. If LS=TRUE, then it is ignored. |
The estimator is the minimizer of
sum_{i=1}^n w_i rho (Y_i - X_i b)
Assuming a correlation model Y_i = X_i β + σ(X_i) ε_i .
the estimator hat β.
Mai Zhou.
Zhou, M.; Bathke, A. and Kim, M. (2006). Empirical likelihood analysis of the Heteroscastic AFT model. Tech. Report.
if(require("rankreg", quietly = TRUE)) { ####library(rankreg) data(smallcell) WRegEst(x=cbind(1,smallcell[,1],smallcell[,2]), y=smallcell[,3], delta=smallcell[,4]) #################################################### #### you should get x1 x2 x3 #### -59.22126 -488.41306 16.03259 #################################################### xx <- c(28,-44,29,30,26,27,22,23,33,16,24,29,24,40,21,31,34,-2,25,19) }