Social Science
Machine Learning, Statistics
Relevant Projects:

Election Forensics.

Walter Mebane


Political Science, LSA
Center for Political Studies
Statistics, LSA

Professor of Political Science, Professor of Statistics, College of Literature, Science, and the Arts and Research Professor, Center for Political Studies, Institute for Social Research

My primary project, election forensics, concerns using statistical analysis to try to determine whether election results are accurate.  Election forensics methods use data about voters and votes that are as highly disaggregated as possible.  Typically this means polling station (precinct) data, sometimes ballot box data.  Data can comprises hundreds of thousands or millions of observations.  Geographic information is used, with geographic structure being relevant.  Estimation involves complex statistical models.  Frontiers include:  distinguishing frauds from effects of strategic behavior;  estimating frauds probabilities for individual observations (e.g., polling stations);  adjoining nonvoting data such as from in-person election observations.