  Loss tomography has received considerable attention in recent years and a
number of estimators have been proposed. Although most of the estimators claim
to be the maximum likelihood estimators, the claim is only partially true since
the maximum likelihood estimate can be obtained at most for a class of data
sets. Unfortunately, few people are aware of this restriction that leads to a
misconception that an estimator is applicable to all data sets as far as it
returns a unique solution. To correct this, we in this paper point out the risk
of this misconception and illustrate the inconsistency between data and model
in the most influential estimators. To ensure the model used in estimation
consistent with the data collected from an experiment, the data sets used in
estimation are divided into 4 classes according to the characteristics of
observations. Based on the classification, the validity of an estimator is
defined and the validity of the most influential estimators is evaluated. In
addition, a number of estimators are proposed, one for a class of data sets
that have been overlooked. Further, a general estimator is proposed that is
applicable to all data classes. The discussion starts from the tree topology
and end at the general topology.
