 Solution

Write $s=e^{-\lambda t}$ for individual survival probability. Conditional on a clump of size $n$, its death probability is $(1-s{)}^n$. Averaging with the geometric distribution givesThusHere $0\le \theta <1$ and $t>0$; different clumps and experimental repetitions are understood independent for the sampling model.

The live-clump counts have independent binomial distributions, all with the same $p$. Up to a constant, their combined likelihood function is $p^R(1-p{)}^{N-R}$, maximized at $\hat{p}=R/N$ when $0<R<N$. Solving $p=s/(1-\theta +\theta s)$ for $s$ gives $s=p(1-\theta )/(1-\theta p)$. ThereforeMonotonicity of $p$ in $\lambda$ makes this the maximum-likelihood estimator. At the sampling boundaries, $R=N$ gives $\hat{\lambda }=0$, while $R=0$ gives only a supremum as $\lambda \to \infty$, with no finite maximizer. These are the limiting interpretations of the formula.