  When the experimental data set is contaminated, we usually employ robust
alternatives to common location and scale estimators such as the sample median
and Hodges-Lehmann estimators for location and the sample median absolute
deviation and Shamos estimators for scale. It is well known that these
estimators have high positive asymptotic breakdown points and are
Fisher-consistent as the sample size tends to infinity. To the best of our
knowledge, the finite-sample properties of these estimators, depending on the
sample size, have not well been studied in the literature. In this paper, we
fill this gap by providing their closed-form finite-sample breakdown points and
calculating the unbiasing factors and relative efficiencies of the robust
estimators through the extensive Monte Carlo simulations up to the sample size
100. The numerical study shows that the unbiasing factor improves the
finite-sample performance significantly. In addition, we provide the predicted
values for the unbiasing factors obtained by using the least squares method
which can be used for the case of sample size more than 100.

Methodology