Physics and Astronomy Building Room 1434A
Mauricio Sadinle
University of Washington

Multiple-systems or capture–recapture estimation are common techniques for population size estimation, particularly in the quantitative study of human rights violations. These methods rely on multiple samples from the population, along with the information of which individuals appear in which samples. The goal of record linkage techniques is to identify unique individuals across samples based on the information collected on them. Linkage decisions are subject to uncertainty when such information contains errors and missingness, and when different individuals have very similar characteristics. Uncertainty in the linkage should be propagated into the stage of population size estimation. We propose an approach called linkage-averaging to propagate linkage uncertainty, as quantified by some Bayesian record linkage methodologies, into a subsequent stage of population size estimation. Linkage-averaging is a two-stage approach in which the results from the record linkage stage are fed into the population size estimation stage. We show that under some conditions the results of this approach correspond to those of a proper Bayesian joint model for both record linkage and population size estimation. The two-stage nature of linkage-averaging allows us to combine different record linkage models with different capture–recapture models, which facilitates model exploration. We present a case study from the Salvadoran civil war, where we are interested in estimating the total number of civilian killings using lists of witnesses’ reports collected by different organizations. These lists contain duplicates, typographical and spelling errors, missingness, and other inaccuracies that lead to uncertainty in the linkage. We show how linkage-averaging can be used for transferring the uncertainty in the linkage of these lists into different models for population size estimation.

Mauricio is an Assistant Professor in the Department of Biostatistics at the University of Washington. Previously, he was a Postdoctoral Associate in the Department of Statistical Science at Duke University and the National Institute of Statistical Sciences, working under the mentoring of Jerry Reiter. Mauricio completed his PhD in the Department of Statistics at Carnegie Mellon University, where his advisor was Steve Fienberg. His undergraduate studies are from the National University of Colombia, in Bogota, where he majored in statistics.

Physics and Astronomy Building Room 1434A
Adeline Lo
Princeton University

This seminar is co-sponsored by the UCLA Center for Social Statistics.

High dimensional (HD) data, where the number of covariates and/or meaningful covariate interactions might exceed the number of observations, is increasing used in prediction in the social sciences. An important question for the researcher is how to select the most predictive covariates among all the available covariates. Common covariate selection approaches use ad hoc rules to remove noise covariates, or select covariates through the criterion of statistical significance or by using machine learning techniques. These can suffer from lack of objectivity, choosing some but not all predictive covariates, and failing reasonable standards of consistency that are expected to hold in most high-dimensional social science data. The literature is scarce in statistics that can be used to directly evaluate covariate predictivity. We address these issues by proposing a variable screening step prior to traditional statistical modeling, in which we screen covariates for their predictivity. We propose the influence (I) statistic to evaluate covariates in the screening stage, showing that the statistic is directly related to predictivity and can help screen out noisy covariates and discover meaningful covariate interactions. We illustrate how our screening approach can removing noisy phrases from U.S. Congressional speeches and rank important ones to measure partisanship. We also show improvements to out-of-sample forecasting in a state failure application. Our approach is applicable via an open-source software package.

Adeline Lo is a postdoctoral research associate at the Department of Politics at Princeton University. Her research lies in the design of statistical tools for prediction and measurement for applied social sciences, with a substantive interest in conflict and post-conflict processes. She has an ongoing research agenda on high dimensional forecasting, especially in application to violent events. Her work has been published in the Proceedings of the National Academy of Sciences, Comparative Political Studies and Nature. She will be joining the Department of Political Science at the University of Wisconsin-Madison as an Assistant Professor in Fall 2019.

Physics and Astronomy Building Room 1434A
Lan Liu
University of Minnesota – Twin Cities

This seminar is co-sponsored by the UCLA Center for Social Statistics.

Abstract: TBA

Physics and Astronomy Building Room 1434A
Eloise Kaizar
Ohio State University

This seminar is co-sponsored by the UCLA Center for Social Statistics.

Abstract: TBA