  Assessing the technical efficiency of a set of observations requires that the
associated data composed of inputs and outputs are perfectly known. If this is
not the case, then biased estimates will likely be obtained. Data Envelopment
Analysis (DEA) is one of the most extensively used mathematical models to
estimate efficiency. It constructs a piecewise linear frontier against which
all observations are compared. Since the frontier is empirically defined, any
deviation resulting from low data quality (imperfect knowledge of data or IKD)
may lead to efficiency under/overestimation. In this study, we model IKD and,
then, apply the so-called Hit \& Run procedure to randomly generate admissible
observations, following some prespecified probability density functions. Sets
used to model IKD limit the domain of data associated with each observation.
Any point belonging to that domain is a candidate to figure out as the
observation for efficiency assessment. Hence, this sampling procedure must run
a sizable number of times (infinite, in theory) in such a way that it populates
the whole sets. The DEA technique is used during the execution of each
iteration to estimate bootstrapped efficiency scores for each observation. We
use some scenarios to show that the proposed routine can outperform some of the
available alternatives. We also explain how efficiency estimations can be used
for statistical inference. An empirical case study based on the Portuguese
public hospitals database (2013-2016) was addressed using the proposed method.
