Explicit Estimators for Loss Tomography

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.

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