Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model B Letham, C Rudin, TH McCormick, D Madigan | 964 | 2015 |
Segregation in social networks based on acquaintanceship and trust TA DiPrete, A Gelman, T McCormick, J Teitler, T Zheng American journal of sociology 116 (4), 1234-1283, 2011 | 350 | 2011 |
Using Twitter for demographic and social science research: tools for data collection and processing TH McCormick, H Lee, N Cesare, A Shojaie, ES Spiro Sociological methods & research 46 (3), 390-421, 2017 | 306 | 2017 |
How many people do you know?: Efficiently estimating personal network size TH McCormick, MJ Salganik, T Zheng Journal of the American Statistical Association 105 (489), 59-70, 2010 | 244 | 2010 |
Racial inequalities in connectedness to imprisoned individuals in the United States H Lee, T McCormick, MT Hicken, C Wildeman Du Bois Review: Social Science Research on Race 12 (2), 269-282, 2015 | 178 | 2015 |
# Proana: Pro-eating disorder socialization on Twitter A Arseniev-Koehler, H Lee, T McCormick, MA Moreno Journal of Adolescent Health 58 (6), 659-664, 2016 | 162 | 2016 |
Promises and pitfalls of using digital traces for demographic research N Cesare, H Lee, T McCormick, E Spiro, E Zagheni Demography 55, 1979-1999, 2018 | 156 | 2018 |
Using aggregated relational data to feasibly identify network structure without network data E Breza, AG Chandrasekhar, TH McCormick, M Pan American Economic Review 110 (8), 2454-2484, 2020 | 152 | 2020 |
Probabilistic cause-of-death assignment using verbal autopsies TH McCormick, ZR Li, C Calvert, AC Crampin, K Kahn, SJ Clark Journal of the American Statistical Association 111 (515), 1036-1049, 2016 | 121 | 2016 |
Dynamic logistic regression and dynamic model averaging for binary classification TH McCormick, AE Raftery, D Madigan, RS Burd Biometrics 68 (1), 23-30, 2012 | 105 | 2012 |
Bayesian hierarchical rule modeling for predicting medical conditions TH McCormick, C Rudin, D Madigan | 99* | 2012 |
Estimating population size using the network scale up method R Maltiel, AE Raftery, TH McCormick, AJ Baraff The annals of applied statistics 9 (3), 1247, 2015 | 77 | 2015 |
Reactive point processes: A new approach to predicting power failures in underground electrical systems Ş Ertekin, C Rudin, TH McCormick | 74 | 2015 |
Latent surface models for networks using aggregated relational data TH McCormick, T Zheng Journal of the American Statistical Association 110 (512), 1684-1695, 2015 | 69 | 2015 |
Methods for correcting inference based on outcomes predicted by machine learning S Wang, TH McCormick, JT Leek Proceedings of the National Academy of Sciences 117 (48), 30266-30275, 2020 | 65 | 2020 |
Latent space models for multiview network data M Salter-Townshend, TH McCormick The annals of applied statistics 11 (3), 1217, 2017 | 65 | 2017 |
Estimating uncertainty in respondent-driven sampling using a tree bootstrap method AJ Baraff, TH McCormick, AE Raftery Proceedings of the National Academy of Sciences 113 (51), 14668-14673, 2016 | 56 | 2016 |
Building interpretable classifiers with rules using Bayesian analysis B Letham, C Rudin, TH McCormick, D Madigan Department of Statistics Technical Report tr609, University of Washington 9 …, 2012 | 50 | 2012 |
Clustering South African households based on their asset status using latent variable models D McParland, IC Gormley, TH McCormick, SJ Clark, CW Kabudula, ... The annals of applied statistics 8 (2), 747, 2014 | 49 | 2014 |
An interpretable stroke prediction model using rules and Bayesian analysis B Letham, C Rudin, TH McCormick, D Madigan Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013 | 48 | 2013 |