Publications

• Fan, S., Visokay, A., Hoffman, K., Salerno, S., Liu, L., Leek, J. T., & McCormick, T. H. (2024). Valid inference using language model predictions from Verbal Autopsy narratives. Accepted, Conference on Language Modeling (COLM).

• Kunke, J. P., Laga, I., Niu, X., & McCormick, T. H. (2024+). Comparing the robustness of simple Network Scale-Up Method (NSUM) estimators. To appear, Sociological Methodology.

• Markwalter, C.F., Lapp, Z., Abel, L., Kimachas, E., Omollo, E., Freedman, E., Chepkwony, T., Amunga, M., McCormick, T. H., Berube, S., Mangeni, J.N., Wesolowski, A., Obala, A., Taylor, S. M., & O’Meara, W. P. (2024+). Mosquito and human characteristics influence natural Anopheline biting behavior and Plasmodium falciparum transmission. To appear, Nature Communications.

• Morrison, L., Brown, E., Paganelli, C. R., Goco, N., & McCormick, T. H. (2024). Enhancing verbal autopsy with MITS: Cost trade-off study, Gates Open Research, 8(40), 40.

• Breza, E., Chandrasekhar, A. G., Lubold, S., McCormick, T. H., & Pan, M. (2023+) Consistently estimating graph statistics using Aggregated Relational Data. To appear, Proceedings of the National Academy of Sciences (USA). overview, code, arxiv version

• Lubold, S, Chandrasekhar, A. G., & McCormick, T. H. (2023+) Identifying the latent space geometry of network models through analysis of curvature. To appear, Journal of the Royal Statistical Society: Series B. overview and code

• Liu, B., Lubold, S., Raftery, A. E., & McCormick, T. H. (2023+) Bayesian hyperbolic multidimensional scaling. To appear, Journal of Computational and Graphical Statistics.

• Yoshida, T., Fan, T. S., McCormick, T., Wu, Z., & Li, Z. R. (2023). Bayesian active questionnaire design for cause-of-death assignment using verbal autopsies. To appear, Conference on Health, Inference, and Learning (CHIL).

• Kunke, J. P., Visokay, A., & McCormick, T. H. (2023+) Respondent-Driven Sampling: An Overview in the Context of Human Trafficking. To appear, CHANCE.

• Marrs, F. W., Fosdick, B. K., & McCormick, T. H. (2023+) Regression of exchangeable relational arrays. To appear, Biometrika (Miscellanea). code

• Li, Z. R., Thomas, J., Choi, E., McCormick, T. H., & Clark, S. J. (2023+). The openVA toolkit for verbal autopsies. To appear, The R Journal.

• Turnbull, K., Nemeth, C., Nunes, M., & McCormick, T. H. (2023+) Sequential estimation of temporally evolving latent space network models. To appear, Computational Statistics and Data Analysis. code

• Boudreau, L. Heath, R., and McCormick, T. H. (2023+) Migrants, information, and working conditions in Bangladeshi garment factories. To appear, Journal of Economic Behavior and Organization.

• Li, C., Rudin, C., & McCormick, T. H. (2022) Rethinking nonlinear instrumental variable models through prediction validity. Journal of Machine Learning Research, 23(96), 1-55.

• Schumacher, A.E., McCormick, T.H., Wakefield, J., Chu, Y., Perin, J., Villavicencio, F., Simon, N. & Liu, L. (2022). A flexible Bayesian framework to estimate age- and cause-specific child mortality over time from sample registration data. Annals of Applied Statistics, 16(1), 124-143.

• Perin, J., Chu, Y., Villaviciencio, F., Schumacher, A., McCormick, T. H., Guillot, M., and Liu, L. (2022) Adapting and validating the log quadratic model to derive under-five age- and cause-specific mortality (U5ACSM): a preliminary analysis. Population Health Metrics 20, 3.

• Arinaminpathy, N., Das, J., McCormick, T., Mukhopadhyay, P., & Sircar, N. (2021). Quantifying heterogeneity in SARS-CoV-2 transmission during the lockdown in India. Epidemics, 36, 100477.

• Lee, W., McCormick, T. H., Neil, J., Sodja, C., & Cui, Y. (2021). Anomaly Detection in Large Scale Networks with Latent Space Models. Technometrics, 1-12. code

• Ng, T. L. J., Murphy, T. B., Westling, T., McCormick, T. H., & Fosdick, B. (2021). Modeling the social media relationships of Irish politicians using a generalized latent space stochastic blockmodel. Annals of Applied Statistics, 15(4), 1923-1944.

• Pan, M., Fosdick, B., & McCormick, T. H. (2021). Inference for network regression models with community structure. In Proceedings of the 38th International Conference on Machine Learning (ICML).

• McCormick, T. H. (2021). The “given data” paradigm undermines both cultures. Observational Studies, 7(1), 157-159.

• Wang, S., McCormick, T. H., & Leek, J. T. (2020). Methods for correcting inference based on outcomes predicted by machine learning. Proceedings of the National Academy of Sciences (USA), 117(48), 30266-30275. R package

• Breza, E., Chandrasekhar, A. G., McCormick, T. H., & Pan, M. (2020) Using Aggregated Relational Data to feasibly identify network structure without network data. American Economic Review, 110, 8.
  • code
  • model fitting instructions/pseudocode
  • implementation instructions and survey instruments.
    
    

• McCormick, T. H. (2020) The Network Scale-up Method. Oxford Handbook of Social Networks.

• Li, Z. R., McCormick, T. H., and Clark, S. J. (2020). Using Bayesian latent Gaussian graphical models to infer symptom associations in verbal autopsies. Bayesian Analysis, 15, 3. code

• Green, A., McCormick, T. H., & Raftery, A. E. (2020). Consistency for the tree bootstrap in respondent-driven sampling. Biometrika (Miscellanea), 107, 2.

• Kunihama, T., Li, Z. R., Clark, S. J., & McCormick, T. H. (2020). Bayesian factor models for probabilistic cause of death assessment with verbal autopsies. Annals of Applied Statistics, 14, 1. code

• Li, Z. R., McCormick, T. H., and Clark, S. J. (2020) Non-confirming Replication of ``Performance of InSilicoVA for Assigning Causes of Death to Verbal Autopsies: Multisite Validation Study using Clinical Diagnostic Gold Standards’’, BMC Medicine 2018; 16:56. BMC Medicine, 18, 69.

• Li, Z. R., McCormick, T. H., and Clark, S. J. (2019). Bayesian Joint Spike-and-Slab Graphical Lasso. In Proceedings of the 36th International Conference on Machine Learning (ICML).

• Li, Z. R., & McCormick, T. H. (2019). An Expectation Conditional Maximization approach for Gaussian graphical models. Journal of Computational and Graphical Statistics, 28, 4. code

• Eadie, G., Huppenkothen, D., Springford, E., & McCormick, T. H. (2019). Introducing Bayesian Analysis with m&m’s: an active-learning exercise for undergraduates. Journal of Statistics Education, 27, 2. code

• Wang, F., McCormick, T. H., Rudin, C., and Gore, J. (2019). Modeling recovery curves with application to Prostatectomy. Biostatistics, 20, 4. code

• Westling, T., & McCormick, T. H. (2019). Beyond prediction: A framework for inference with variational approximations in mixture models. Journal of Computational and Graphical Statistics, 28, 4. code

• Jha, P., Kumar, D., Dikshit, R., Budukh, A., Begum, R., Sati, P., Kolpak, P., Wen, R., Raithatha, S.J., Shah, U., Li, Z.R., Aleksandrowicz, L., Shah, P., Piyasena, K., McCormick, T. H., Gelband, H. & Clark, S. J. (2019) Automated versus physician assignment of cause of death for verbal autopsies: randomized trial of 9374 deaths in 117 villages in India. BMC Medicine, 17, 116.

• Fosdick, B., McCormick, T. H., Murphy, T. B., Ng, T. L., and Westling, T. (2018). Multiresolution network models. Journal of Computational and Graphical Statistics, 28, 185-196. code

• Cesare, N., Lee, H., McCormick, T. H., Spiro, E., and Zagheni, E. (2018). Promises and pitfalls of using digital traces for demographic research. Demography, 55, 1979-1999.

• Lee, W., Fosdick, B., and McCormick, T. H. (2018). Inferring social structure from continuous-time interaction data. Discussion paper. Applied Stochastic Models in Business and Industry, 34, 87-104. code

• Clark, S., Wakefield, J, McCormick, T. H., and Ross, M. (2018). Hyak mortality monitoring system: Innovative sampling and estimation methods. Global Health, Epidemiology and Genomics, 3, E3.

• Salter-Townshend, M. and McCormick, T. H. (2017). Latent space models for multiview network data. Annals of Applied Statistics, 11: 1217-1244. code

• Baraff, A., McCormick, T. H., and Raftery, A. E. (2016). Estimating Uncertainty in Respondent-Driven Sampling Using a Tree Bootstrap Method. Proceedings of the National Academy of Sciences (USA), 113: 14668-14673. R package

• McCormick, T. H., Li, Z., Calvert, C., Crampin, A. C., Kahn, K., and Clark, S. J. (2016). Probabilistic Cause-of-death Assignment using Verbal Autopsies. Journal of the American Statistical Association, 111: 1036-1049. R package

• Arseniev-Koehler, A., Lee, H., McCormick, T. H., and Moreno, M. (2016). #Proana: Pro-Eating Disorder Socialization on Twitter. Journal of Adolescent Health, 58: 659-664.

• McCormick, T. H. and Zheng, T. (2015). Latent surface models for networks using Aggregated Relational Data, Journal of the American Statistical Association, 110:1684-1695. code

• Letham, B., Rudin, C., McCormick, T. H., and Madigan, D. (2015). Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model. Annals of Applied Statistics, 9:1350-1371. code

• Ertekin, S., Rudin, C, and McCormick, T. H. (2015). Predicting power failures with Reactive Point Processes. Annals of Applied Statistics, 9: 122-144. code

• McCormick, T. H., Lee, H., Cesare, N., Shojaie, A., and Spiro, E. (2015). Using Twitter for Demographic and Social Science Research: Tools for Data Collection. Sociological Methods and Research, 1-32.

• Lee, H., McCormick, T. H., Wildeman, C., and Hicken, M. (2015). Racial inequalities in connectedness to imprisoned individuals in the United States. Du Bois Review: Social Science Research on Race, 12: 269-282.

• Maltiel, R., Raftery, A., McCormick, T. H., and Baraff, A. (2015). Estimating Population Size Using the Network Scale Up Method. Annals of Applied Statistics, 9:1247-1277. R package

• Westling, T., and McCormick, T. H. (2014). Sandwich Covariance Estimation for Variational Inference. NIPS Workshop on Advances in Variational Inference. code

• McParland, D., Gormley, I. C., McCormick, T. H., Clark, S. J., Kabudula, C., and Collison, M. (2014). Clustering South African households based on their asset status using latent variable models. Annals of Applied Statistics, 8: 747-776. code

• McCormick, T. H., Ferrell, R., Karr, A., and Ryan, P. B. (2014). Knowledge Discovery in Output from Large-Scale Medical Analytics. Statistical Learning & Data Mining, 7:404-412.

• Rudin, C., Ertekin, S., Passonneau, R., Radeva, A., Tomar, A., Xie, B., Lewis, S., Riddle, M., Pangsrivinij, D, and McCormick, T. H. (2014). Analytics for Power Grid Distribution Reliability in New York City. Interfaces, 44: 364-383.

• Young, W., Blumenstock J. E., Fox, E. B., and McCormick, T. H. (2014). Detecting and classifying anomalous behavior in spatiotemporal network data. The 20th ACM Conference on Knowledge Discovery and Mining (KDD ‘14), Workshop on Data Science for Social Good, New York, NY.

• McCormick, T. H., and Zheng, T. (2013). Network-based methods for accessing hard-to-reach populations using standard surveys. In Hard-to-Survey Populations. Editors K. Wolter and R. Tourangeau.

• McCormick, T. H., Ruf, J., Moussa, A., Diprete, T. D., Gelman, A., Teitler, J., and Zheng, T. (2013). A practical guide to measuring social structure using indirectly observed network data. Journal of Statistical Theory and Practice, 7:120-132.

• McCormick, T. H., and Zheng, T. (2012). Latent demographic profile estimation in at-risk populations. Annals of Applied Statistics, 6: 1795-1813.

• McCormick, T. H., Rudin, C., and Madigan, D. (2012). A hierarchical model for association rule mining of sequential events: an approach to automated medical symptom prediction. Annals of Applied Statistics, 6: 652-668.

• McCormick, T. H., He, R., Kolaczyk, E., and Zheng, T. (2012). Surveying hard-to-reach groups through sampled respondents in a social network: A comparison of two survey strategies. Statistics in Biosciences, 4: 177-195.

• Diprete, T. D., Gelman, A., McCormick, T. H., Teitler, J., and Zheng, T. (2011). Segregation in social networks based on acquaintanceship and trust. American Journal of Sociology, 116, 1234-83.

• McCormick, T. H., Raftery, A. E., Madigan, D., and Burd, R. (2011). Dynamic logistic regression and dynamic model averaging for binary classification. Biometrics, 68, 23-30. R package

• McCormick, T. H. (2011). Bayesian analysis of social network data. ISBA Bulletin, 18, 6-9.

• McCormick, T. H., Salganik, M. J. and Zheng, T. (2010). How many people do you know?:Efficiently estimating personal network size. Journal of the American Statistical Association, 105, 59-70.

• McCormick, T. H. and Zheng, T. (2010). A latent space representation of overdispersed relative propensity in ‘How many X’s do you know?’ data. in Conference Proceedings of the Joint Statistical Meetings, Vancouver, B.C.

• McCormick, T. H. and Zheng, T. (2009). Towards a unified framework for inference in Aggregated Relational Data in Conference Proceedings of the Joint Statistical Meetings, Washington, D.C.

• McCormick, T. H., Ruf, J., Moussa, A., Diprete, T. D., Gelman, A., Teitler, J., and Zheng, T. (2009). Measuring social distance using indirectly observed network data. in Conference Proceedings of the Joint Statistical Meetings, Washington, D.C.

• McCormick, T. H. and Zheng, T. (2007). Adjusting for recall bias in ‘How many X’s do you know?’ surveys. in Conference Proceedings of the Joint Statistical Meetings, Salt Lake City, Utah.