  Full likelihood has been widely used in loss tomography because most believe
it can produce accurate estimates although the full likelihood estimators
proposed so far are complex in structure and expensive in execution. We in this
paper advocate a different likelihood called composite likelihood to replace
the full likelihood in loss tomography for simplicity and accuracy. Using the
proposed likelihood, we propose a number of explicit estimators with
statistical analysis. The analysis shows all of the explicit estimators perform
almost as good as the full likelihood one in terms of accuracy and better than
the full likelihood one in computational complexity. Although the discussion is
restricted to the tree topology, the methodology proposed here is also
applicable to a network of a general topology.
