= Solution
Under <independent censoring>, observation of an event-free individual to $x_i$ contributes \b[the survival probability]
$$
\boxed{L_i=F(x_i).}
$$
Here $F$ denotes the <survivor function> as in the paper, rather than a cumulative distribution function. The <censoring> distribution contributes an additional factor to the full observed-data likelihood, but if it has distinct parameters and the required independence holds, that factor can be omitted when estimating the event-time parameters. A censored observation still conveys that the event time exceeded $x_i$.
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