[gov3009-l] Applied Statistics 3/29/17 - Kosuke Imai
pban at fas.harvard.edu
Mon Mar 27 09:26:22 EDT 2017
This week at the Applied Statistics workshop we will be welcoming Kosuke Imai, a Professor in the Department of Politics and Center for Statistics and Machine Learning at Princeton University. He will be presenting work entitled "Using a Probabilistic Model to Assist Merging of Large-scale Administrative Records." Please find the abstract below and on the website.
We will meet in CGIS Knafel Room 354 at noon and lunch will be provided.
Title: Using a Probabilistic Model to Assist Merging of Large-scale Administrative Records
Since most social science research relies upon multiple data
sources, merging data sets is an essential part of workflow for many
researchers. In many situations, however, a unique identifier that
unambiguously links data sets is unavailable and data sets may
contain missing and inaccurate information. As a result,
researchers can no longer combine data sets ``by hand'' without
sacrificing the quality of the resulting merged data set. This
problem is especially severe when merging large-scale administrative
records such as voter files. The existing algorithms to automate the
merging process do not scale, result in many fewer matches, and
require arbitrary decisions by researchers. To overcome this
challenge, we develop a fast algorithm to implement the canonical
probabilistic model of record linkage for merging large data sets.
Researchers can combine this model with a small amount of human
coding to produce a high-quality merged data set. The proposed
methodology can handle millions of observations and account for
missing data and auxiliary information. We conduct simulation
studies to show that our algorithm performs well in a variety of
practically relevant settings. Finally, we use our methodology to
merge the campaign contribution data (5 million records), the
Cooperative Congressional Election Study data (50 thousand records),
and the nationwide voter file (160 million records).
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