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Bias reduction in the population size estimation of large data sets
کاهش تمایل در برآورد اندازه جمعیت مجموعه داده های بزرگ-2020 Estimation of the population size of large data sets and hard to reach populations
can be a significant problem. For example, in the military, manpower
is limited and the manual processing of large data sets can be time consuming.
In addition, accessing the full population of data may be restricted by
factors such as cost, time, and safety. Four new population size estimators
are proposed, as extensions of existing methods, and their performances are
compared in terms of bias with two existing methods in the big data literature.
These would be particularly beneficial in the context of time-critical
decisions or actions. The comparison is based on a simulation study and
the application to five real network data sets (Twitter, LiveJournal, Pokec,
Youtube, Wikipedia Talk). Whilst no single estimator (out of the four proposed)
generates the most accurate estimates overall, the proposed estimators
are shown to produce more accurate population size estimates for small sample
sizes, but in some cases show more variability than existing estimators in
the literature. Keywords: Relative bias | Twitter | Size estimator | Youtube | Random walk sampling |
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