Paper prepared for the 2006 Annual Meeting of the Midwest Political Science Association, Chicago, IL.
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
Abstract: This paper introduces statistical methods intended to help detect election fraud. Other methods, using regression-based techniques for outlier detection, have previously been proposed to help detect election anomalies (e.g. Wand, Shotts, Sekhon, Mebane, Herron, and Brady 2001; Mebane, Sekhon, and Wand 2001). The methods described here are distinctive in that they do not require that we have covariates to which we may reasonably assume the votes are related across political jurisdictions. For one set of methods I describe—methods based on tests of the distribution of the digits in reported vote counts—all that is needed are the vote counts themselves. I study the application of those methods to both precinct-level and voting machine-level vote tabulations. Part of the potential practical relevance of these methods is that situations in which little more than the vote counts are available may arise frequently in connection with actual election controversies.
Bibtex:
@inproceedings{,
title={Detecting attempted election theft: vote counts, voting machines and Benford’s law},
author={Mebane Jr, Walter R.},
booktitle={Annual Meeting of the Midwest Political Science Association, Chicago, IL, April},
pages={20--23},
year={2006}
}
Reference Type: E-Print
Subject Area(s): Voting Fraud