Walter R Mebane Jr
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
- Email Walter R Mebane Jr
- (734)763-2220
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BIO
Walter Richard Mebane, Jr. is a University of Michigan professor of political science and statistics and an expert on detecting electoral fraud. He is a research professor with the Center for Political Studies.
- Walter R Mebane Jr. 2019. Voting Technologies, Recount Methods and Votes in Wisconsin and Michigan in 2016. Financial Cryptography and Data Security :196-209.
- Mebane Jr,Walter R. 2017. Comment on ‘Benford’s Law and the Detection of Election Fraud’. Comment on ‘Benford’s Law and the Detection of Election Fraud’ 19(3):269-272.
- Jonathan N. Wand, Kenneth W. Shotts, Jasjeet S. Sekhon, Walter R Mebane Jr, Michael C. Herron, Henry E. Brady. 2017. The Butterfly Did It: The Aberrant Vote for Buchanan in Palm Beach County, Florida. American Political Science Review 95(4):793-810.
- Mebane Jr, Walter R. 2015. Can Vote Counts' Digits and Benford's Law Diagnose Elections?. Benford's Law : Theory and Applications :212-222.
- Mebane Jr, Walter R, Poast, Paul . 2013. Causal Inference without Ignorability: Identification with Nonrandom Assignment and Missing Treatment Data. Political Analysis 21(2):233-251.
- Mebane Jr, Walter R. 2011. Comment on "Benford's Law and the Detection of Election Fraud". Political Analysis 19(3):269-272.
- Mebane Jr, Walter R, Sekhon, Jasjeet S. 2011. Genetic Optimization Using Derivatives: The rgenoud Package for R. Journal of Statistical Software 42(11):1-26.
- Walter R Mebane Jr, Sekhon, J.S. 2004. Robust Estimation and Outlier Detection for Overdispersed Multinomial Models of Count Data. American Journal of Political Science 48(2):392-411.
- Walter R Mebane Jr. 1994. Fiscal Constraints and Electoral Manipulation in American Social Welfare. Cambridge University Press 88(1):77-94.
- Mebane Jr,Walter R. . Causal Inference without Ignorability: Identification with Nonrandom Assignment and Missing Treatment Data. Causal Inference without Ignorability: Identification with Nonrandom Assignment and Missing Treatment Data 21(2):233-251.