  Numerous robust estimators exist as alternatives to the maximum likelihood
estimator (MLE) when a completely observed ground-up loss severity sample
dataset is available. However, the options for robust alternatives to MLE
become significantly limited when dealing with grouped loss severity data, with
only a handful of methods like least squares, minimum Hellinger distance, and
optimal bounded influence function available. This paper introduces a novel
robust estimation technique, the Method of Truncated Moments (MTuM),
specifically designed to estimate the tail index of a Pareto distribution from
grouped data. Inferential justification of MTuM is established by employing the
central limit theorem and validating them through a comprehensive simulation
study.
