= Likelihood support reduction under censoring
{title2=$L(w)=\prod_i\left(\sum_j A_{ij}w_j\right)$}
Partition the time axis into cells on which membership in each observed exact-event or <censoring> set is constant. Let $w_j$ be a cell's probability mass and $A_{ij}$ indicate whether cell $j$ is permitted by observation $i$. The <empirical likelihood> has the displayed form, with nonnegative masses summing to one. Columns of $A$ that agree can be combined. If column $j$ is componentwise dominated by another column $k$, moving all mass from $j$ to $k$ cannot reduce any factor, so an unconstrained <nonparametric maximum-likelihood estimator> can omit column $j$. The remaining <log-likelihood> is a <concave function> of the masses. Extra constraints can prevent a domination move; for a fixed <survivor function> value, split cells at the constrained time and retain the associated linear mass constraint.
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