Papers & Software
Department of Statistics
Department of Sociology
University of Washington
Seattle, WA 98195-4322
(206) 221-6873 (fax)
Current technical reports and working papers
Bayesian Joint Spike-and-Slab Graphical Lasso (with Z. R. Li and S. Clark). 2018.
Quantifying the Contributions of Training Data and Algorithm Logic to the Performance of Automated Cause-assignment Algorithms for Verbal Autopsy (with Z. R. Li and S. Clark). 2018.
Bayesian factor models for probabilistic cause of death assessment with verbal autopsies (with T. Kunihama, Z. R. Li and S. Clark). 2018.
Bayesian latent Gaussian graphical models for mixed data with marginal prior information (with Z. R. Li and S. Clark). 2017.
An Expectation Conditional Maximization approach for Gaussian graphical models (with Z. R. Li). 2017.
Using Aggregated Relational Data to feasibly identify network structure without network data (with E. Breza, A. Chandrasekhar and M. Pan). 2017.
Standard errors for regression on relational data with exchangeable errors (with B. Fosdick and F. Marrs). 2017.
Consistency, Calibration, and Efficiency of Variational Inference (with T. Westling). 2017.
Examining Racial Segregation in Associative Networks on Twitter (with N. Cesare, E. Spiro, and H. Lee). 2017.
Migrants, information, and working conditions in Bangladeshi garment factories (with L. Boudreau and R. Heath). 2016.
Online Information Behaviors During Disaster Events: Roles, Routines, and Reactions (with E. Spiro and H. Reeder). 2014.
Improving attribute prediction through Network-Augmented Prediction (with A. Zimmerman, H. Lee, and A. Shojaie). 2013. pdf
Fosdick, B., McCormick, T. H., Murphy, T. B., Ng, T. L., and Westling, T. (2018+) Multiresolution network models. To appear, Journal of Computational and Graphical Statistics.pdfslidescode
Cesare, N., Lee, H., McCormick, T. H., Spiro, E., and Zagheni, E. (2018+) Promises and pitfalls of using digital traces for demographic research. To appear, Demography.pdf
Wang, F., McCormick, T. H., Rudin, C., and Gore, J. (2018+) Modeling recovery curves with application to Prostatectomy. To appear, Biostatistics.pdfcode
Lee, W., Fosdick, B., and McCormick, T. H. (2018+) Inferring social structure from continuous-time interaction data. Discussion paper. To appear, Applied Stochastic Models in Business and Industry. pdfcode
Clark, S., Wakefield, J, McCormick, T. H., and Ross, M. (2018+) Hyak mortality monitoring system: Innovative sampling and estimation methods. To appear, Global Health, Epidemiology and Genomics.
Salter-Townshend, M. and McCormick, T. H. (2017) Latent space models for multiview network data. Annals of Applied Statistics, 11: 1217-1244. pdfsupplement/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. pdfsoftware
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. pdfsupplementary materialsoftware
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. pdf
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. pdf
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.
Ertekin, S., Rudin, C, and McCormick, T. H. (2015). Predicting power failures with Reactive Point Processes. Annals of Applied Statistics, 9: 122-144.
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. pdf
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. pdf
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. pdfsoftware
Westling, T., and McCormick, T. H. (2014) Sandwich Covariance Estimation for Variational
Inference. NIPS Workshop on Advances in Variational Inference.pdfcode
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. pdfsupplement/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. pdf
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. pdf
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. pdf
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. pdf
McCormick, T. H., and Zheng, T. (2012) Latent demographic profile estimation in at-risk populations. Annals of Applied Statistics, 6: 1795-1813. pdf
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. pdf
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. pdf
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. pdf
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. pdf
McCormick, T. H. (2011). Bayesian analysis of social network data. ISBA Bulletin, 18, 6-9. pdf
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. pdf
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. pdf
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. pdf
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. pdf