Log-Concavity of Multinomial Likelihood Functions Under Interval
  Censoring Constraints on Frequencies or Their Partial Sums
Bruce Levin Erik Learned-Miller
We show that the likelihood function for a multinomial vector observed under
arbitrary interval censoring constraints on the frequencies or their partial
sums is completely log-concave by proving that the constrained sample spaces
comprise M-convex subsets of the discrete simplex.