Presentations

Brandon Stewart (Harvard) - Latent Factor Regressions for the Social Sciences Wednesday, September 24, 2014

Abstract: I present a general framework for regression in the presence of complex dependence structures between units such as in time-series cross-sectional data, relational/network data, and spatial data. These types of data are challenging for standard multilevel models because they involve multiples types of structure (e.g. temporal effects and cross-sectional effects) which are interactive. I show that interactive latent factor models provide a powerful modeling alternative that can address a wide range of data types. Although, related models have...

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James Lloyd (University of Cambridge) - The Automatic Statistician Wednesday, September 17, 2014

Abstract:  While it is becoming easier to collect and store all kinds of data, including personal medical data, scientific data, and commercial data, there are relatively few people trained in the statistical and machine learning methods required to test hypotheses, make predictions, and otherwise create interpretable knowledge from this data. The automatic statistician project aims to build an artificial intelligence for data science, to help people make sense of their data and to uncover challenging research problems in automatic data...

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Matt Blackwell (Harvard) - Game-changers: Detecting shifts in the flow of campaign contributions Wednesday, September 10, 2014

Abstract: In this paper, I introduce a Bayesian model for detecting changepoints in a time-series of contributions to candidates over the course of a campaign. This game-changers model is ideal for campaign contributions data because it allows for overdispersion, a key feature of contributions data. Furthermore, while many extant changepoint models force researchers to choose the number of changepoint ex ante, the game-changers model incorporates a Dirichlet process prior in order to estimate the number of changepoints along with their location. I demonstrate...

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Eric Chaney (Harvard) - The Medieval Origins of Comparative European Development: Evidence from the Basque Country Wednesday, September 3, 2014

Abstract: This paper investigates the present-day economic impact of medieval republican institutions along the historical borders of the Basque Country in Spain and France. I present evidence suggesting that medieval republican institutions have had a lasting effect: in Spain the drop in incomes along the Basque border is similar to that between the richest and poorest areas of the euro zone today. Using present-day and historical data, I investigate the mechanisms through which these medieval institutions have had enduring effects. Although I find evidence...

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Nathan Kallus (MIT) - Regression-Robust Designs of Controlled Experiments Wednesday, April 30, 2014

Abstract: Achieving balance between experimental groups is a cornerstone of causal inference. Without balance any observed difference may be attributed to a difference other than the treatment alone. In controlled/clinical trials, where the experimenter controls the administration of treatment, complete randomization of subjects has been the golden standard for achieving this balance because it allows for unbiased and consistent estimation and inference in the absence of any a priori knowledge or measurements. However, since estimator variance under complete...

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Nick Beauchamp (Northeastern University) - Predicting, Extrapolating and Interpolating State-level Polls using Twitter Wednesday, April 23, 2014

Abstract: Presidential, gubernatorial, and senatorial elections all require state-level polling, but even during presidential campaigns, state-level surveys remain sparse, erratically timed, and entirely neglected in uncompetitive states. Partly in response to these unmet needs in political and other domains, there have been numerous efforts to approximate various survey measures using social media data, but most of these approaches remain distinctly flawed, both methodologically and due to insufficient training data.  To remedy these flaws, this paper...

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Finale Doshi (Harvard) - Prediction and Interpretation with Latent Variable Models Wednesday, April 16, 2014

Abstract: Latent variable models provide a powerful tool for summarizing data through a set of hidden variables.  These models are generally trained to maximize prediction accuracy, and modern latent variable models now do an excellent job of finding compact summaries of the data with high predictive power.  However, there are many situations in which good predictions alone are not  sufficient. Whether the hidden variables have inherent value by providing insights  about the data, or whether we simply wish to improve a system, understanding...

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Eleanor Neff Powell (Yale) - Money in Exile: Campaign Contributions and Committee Access Wednesday, April 9, 2014:

 Abstract: Corporations and political action committees (PACs) flood congressional elections with money. Understanding why they contribute is essential for determining how money in- fluences policy in Congress. To test theories of contributors’ motivations we exploit committee exile—the involuntary removal of committee members after a party loses a sizable number of seats, and the losses are unevenly distributed across committees. We use exile to show that business...

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Krista Gile (University of Massachusetts Amherst) - New methods for inference from Respondent-Driven Sampling Data Wednesday, April 2, 2014

Abstract: Respondent-Driven Sampling is type of link-tracing network sampling used to study hard-to-reach populations.  Beginning with a convenience sample, each person sampled is given 2-3 uniquely identified coupons to distribute to other members of the target population, making them eligible for enrollment in the study.  This is effective at collecting large diverse samples from my populations.

Current estimation relies on sampling weights estimated by treating the sampling process as a random walk on the underlying network of social...

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Herman van Dijk (Vrije Universiteit) - Forecasting with Many Models in Finance and Economics using Large Data Sets and Parallel Computing Wednesday, March 26, 2014:

Abstract: We propose a Bayesian combination approach for multivariate predictive densities which relies upon a distributional state space representation of the combination weights. Several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics driven by the past performance of the predictive densities and the use of learning mechanisms. In the proposed approach the model set can be incomplete, meaning that all models can be individually misspecified. A Sequential Monte Carlo method is proposed to...

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