DPIR TechTalks - 'Ecological inference with distribution regression: Voting behaviour in US elections'
Come along to hear Seth Flaxman from the Department of Statistics to explain how he has used data to enable predictive analysis of US elections.
By using ecological inference and census data, Seth has estimated not only “exit poll” style results (such as Trump’s level of support among white women), but entirely novel categories. The data analysis has also allowed researchers to explore which characteristics listed in the census predict voting behaviour, and non-voting.
Seth Flaxman is a postdoc with Yee Whye Teh at Oxford in the computational statistics and machine learning group in the Department of Statistics. His research is on scalable methods and flexible models for spatiotemporal statistics and Bayesian machine learning, applied to public policy and social science areas including crime, emotion, and public health.