https://github.com/cran/emplik
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Tip revision: 6499006531aa58c62dc136e7a2daf03dfbc5aa36 authored by Mai Zhou on 07 September 2023, 17:00:02 UTC
version 1.3-1
Tip revision: 6499006
BJnoint.Rd
\name{BJnoint}
\alias{BJnoint}
\title{The Buckley-James censored regression estimator}
\usage{
BJnoint(x, y, delta, beta0 = NA, maxiter=30, error = 0.00001)
}
\arguments{
    \item{x}{a matrix or vector containing the covariate, one row
 	 per observation.}
    \item{y}{a numeric vector of length N, censored responses. }
    \item{delta}{a vector of length N, delta=0/1 for censored/uncensored.}
    \item{beta0}{an optional vector for starting value of iteration.}
    \item{maxiter}{an optional integer to control iterations.}
    \item{error}{an optional positive value to control iterations.}
}
\description{
    Compute the Buckley-James estimator in the regression model 
\deqn{ y_i = \beta x_i + \epsilon_i } 
with right censored \eqn{y_i}. Iteration method.

}
\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
\code{bj( )} in the \code{Design} library. It should be more refined 
than \code{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 \code{bjtest( )}.
For example you may want to test a beta value near the
Buckley-James estimator. 

}
\value{
    A list with the following components:
    \item{beta}{the Buckley-James estimator.}
    \item{iteration}{number of iterations performed.}
}
\references{
    Buckley, J. and James, I. (1979).  Linear regression with censored data.
   \emph{Biometrika}, \bold{66} 429-36.
}
\author{ Mai Zhou. }
\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)
}
\keyword{nonparametric}
\keyword{htest}
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