  This paper derives the nonparametric maximum likelihood estimator (NPMLE) of
a distribution function from observations which are subject to both bias and
censoring. The NPMLE is obtained by a simple EM algorithm which is an extension
of the algorithm suggested by Vardi (Biometrika, 1989) for size biased data.
Application of the algorithm to many models is discussed and a simulation study
compares the estimator's performance to that of the product-limit estimator
(PLE). An example demonstrates the utility of the NPMLE to data where the PLE
is inappropriate.
