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:
Diogo Ferrari, Kevin McAlister, and Walter Mebane (2018) Developments in Positive Empirical Models of Election Frauds: Dimensions and Decisions Polmeth XXXV.
Diogo Ferrari, Walter R. Mebane Jr. (2017). Developments in Positive Empirical Models of Election Frauds. Polmeth XXXIV.
Diogo Ferrari, Walter R. Mebane Jr. (2016). Positive Empirical Models of Election Frauds. APSA.
Diogo Ferrari (2016). A Logistic-Binomial Mixture Model for Fraud Detection in Elections. Polmeth XXXIII.