Computation of the MLE for Bivariate Interval Censored Data
Description
We provide functions to compute the nonparametric
maximum likelihood estimator (MLE) for
the bivariate distribution of (X,Y), when
realizations of (X,Y) cannot be observed directly.
To be more precise, we consider the situation
where we observe a set of rectangles in R^2 that are known
to contain the unobservable realizations of (X,Y). We
compute the MLE based on such a set of rectangles.
The methods can also be used for univariate censored data (see data set
'cosmesis'), and for
censored data with competing risks (see data set 'menopause').
We also provide functions to visualize the observed data and the MLE.