  This study deals with a fairly simply formulated problem -- how to estimate
the number of people bearing the same full name in a large population.
Estimation of name popularity can leverage personal name matching in databases
and be of interest for many other domains. A distinctive feature of large
collections of names is that they contain a large number of unique items, which
is challenging for statistical modeling. We investigate a number of statistical
techniques and also propose a simple yet effective method aimed at obtaining
more accurate count estimates. In our experiments we use a dataset containing
about 20 million name occurrences that correspond to about 13 million
real-world persons. We perform a thorough evaluation of the name count
estimation methods and a record linkage experiment guided by name popularity
estimates. Obtained results suggest that theoretically informed approaches
outperform simple heuristics and can be useful in a variety of applications.
