AJPS · 2018
Political scientists are drowning in data — large surveys, social media, administrative records — but their standard statistical toolkit was built for a simpler world. This paper …
PA · 2015
Develops a statistically principled approach to detecting electoral fraud using forensic analysis of vote return distributions. By incorporating prior knowledge about legitimate …
IJF · 2014
Addresses the challenge of calibrating ensemble forecasting models when training data are sparse, a common problem in social science prediction. The paper develops methods for …
PS · 2013
A brief methodological contribution outlining principled approaches to combining data from multiple sources through ensemble and aggregation methods. Argues for a coherent Bayesian …
PA · 2012
Extends Bayesian model averaging to ensemble methods, showing how combining multiple models improves prediction in political science. The paper demonstrates practical gains in …
PS · 2012
Applies ensemble forecasting methods to predict the outcome of the 2012 US presidential election. Demonstrates the practical utility of combining multiple models, with the ensemble …
PA · 2010
Provides a theoretical and practical introduction to Bayesian model averaging for political scientists. The paper shows how BMA improves out-of-sample prediction and addresses …