  The Roman Empire shaped Western civilization, and many Roman principles are
embodied in modern institutions. Although its political institutions proved
both resilient and adaptable, allowing it to incorporate diverse populations,
the Empire suffered from many internal conflicts. Indeed, most emperors died
violently, from assassination, suicide, or in battle. These internal conflicts
produced patterns in the length of time that can be identified by statistical
analysis. In this paper, we study the underlying patterns associated with the
reign of the Roman emperors by using statistical tools of survival data
analysis. We consider all the 175 Roman emperors and propose a new power-law
model with change points to predict the time-to-violent-death of the Roman
emperors. This model encompasses data in the presence of censoring and
long-term survivors, providing more accurate predictions than previous models.
Our results show that power-law distributions can also occur in survival data,
as verified in other data types from natural and artificial systems,
reinforcing the ubiquity of power law distributions. The generality of our
approach paves the way to further related investigations not only in other
ancient civilizations but also in applications in engineering and medicine.
