BJnoint {emplik}

The Buckley-James censored regression estimator

Description

Compute the Buckley-James estimator in the regression model

y_i = \beta x_i + \epsilon_i

with right censored y_i. Iteration method.

Usage

BJnoint(x, y, delta, beta0 = NA, maxiter=30, error = 0.00001)

Arguments

x

a matrix or vector containing the covariate, one row per observation.

y

a numeric vector of length N, censored responses.

delta

a vector of length N, delta=0/1 for censored/uncensored.

beta0

an optional vector for starting value of iteration.

maxiter

an optional integer to control iterations.

error

an optional positive value to control iterations.

Details

This function compute the Buckley-James estimator when your model do not have an intercept term. Of course, if you include a column of 1's in the x matrix, it is also OK with this function and it is equivalent to having an intercept term. If your model do have an intercept term, then you could also (probably should) use the function bj( ) in the Design library. It should be more refined than BJnoint in the stopping rule for the iterations.

This function is included here mainly to produce the estimator value that may provide some useful information with the function bjtest( ). For example you may want to test a beta value near the Buckley-James estimator.

Value

A list with the following components:

beta

the Buckley-James estimator.

iteration

number of iterations performed.

Author(s)

Mai Zhou.

References

Buckley, J. and James, I. (1979). Linear regression with censored data. Biometrika, 66 429-36.

Examples

x <- matrix(c(rnorm(50,mean=1), rnorm(50,mean=2)), ncol=2,nrow=50)
## Suppose now we wish to test Ho: 2mu(1)-mu(2)=0, then
y <- 2*x[,1]-x[,2]
xx <- c(28,-44,29,30,26,27,22,23,33,16,24,29,24,40,21,31,34,-2,25,19)