Unsupervised Learning Methods and FMM for Election Forensics (with Walter Mebane)

Elections are one of the cornerstones of modern democracies. Election forensics is a subarea of Political Science that uses statistical methods to investigate frauds in election.

Frauds are by their very nature concealed phenonema. The perpetrators don’t want to reveal their act and want to avoid leaving any indication of manipulaiton of the results. This NFS-funded project, with Walter Mebane (PA), proposes some unsupervised learning methods and a series of finite mixture models to estimate the probability of fraud in elections using election data.

Some related papers: