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Tyler Mccormick

Position

Professor, Statistics

Research Interests

Bayesian Methods, Social Networks

Bio

Joint Position with Sociology My work develops statistical models targeting questions in sociology, demography, and health. My main focus areas are developing statistical models that parsimoniously represent high-dimensional, complex dependence structure in social networks; estimating vital rates from sparsely sampled data; and incorporating uncertainty and interpretability in predictive models. I'm currently a faculty member in the Statistics and Sociology Departments and a Senior Data Science Fellow in the eScience Institute. My Ph.D. is from Columbia University (Statistics).

Preprints

Estimating and Correcting Degree Ratio Bias in the Network Scale-up Method
Ian Laga, Jessica P. Kunke, Tyler H. McCormick, Xiaoyue Niu
The Network Scale-up Method (NSUM) uses social networks and answers to "How many X's do you know?" questions to estimate sizes of groups excluded by standard…

General Covariance-Based Conditions for Central Limit Theorems with Dependent Triangular Arrays
Arun G. Chandrasekhar, Matthew O. Jackson, Tyler H. McCormick, Vydhourie Thiyageswaran
We present a general central limit theorem with simple, easy-to-check covariance-based sufficient conditions for triangular arrays of random vectors when all…

Feasible contact tracing
Aparajithan Venkateswaran, Jishnu Das, Tyler H. McCormick
Contact tracing is one of the most important tools for preventing the spread of infectious diseases, but as the experience of COVID-19 showed, it is also next…

Non-robustness of diffusion estimates on networks with measurement error
Arun G. Chandrasekhar, Paul Goldsmith-Pinkham, Tyler H. McCormick, Samuel Thau, Jerry Wei
Network diffusion models are used to study things like disease transmission, information spread, and technology adoption. However, small amounts of…

Bayesian analysis of verbal autopsy data using factor models with age- and sex-dependent associations between symptoms
Tsuyoshi Kunihama, Zehang Richard Li, Samuel J. Clark, Tyler H. McCormick
Verbal autopsies (VAs) are extensively used to investigate the population-level distributions of deaths by cause in low-resource settings without well…

From Narratives to Numbers: Valid Inference Using Language Model Predictions from Verbal Autopsy Narratives
Shuxian Fan, Adam Visokay, Kentaro Hoffman, Stephen Salerno, Li Liu, Jeffrey T. Leek, Tyler H. McCormick
In settings where most deaths occur outside the healthcare system, verbal autopsies (VAs) are a common tool to monitor trends in causes of death (COD). VAs are…

Dempster-Shafer P-values: Thoughts on an Alternative Approach for Multinomial Inference
Kentaro Hoffman, Kai Zhang, Tyler McCormick, Jan Hannig
In this paper, we demonstrate that a new measure of evidence we developed called the Dempster-Shafer p-value which allow for insights and interpretations which…

Do We Really Even Need Data?
Kentaro Hoffman, Stephen Salerno, Awan Afiaz, Jeffrey T. Leek, Tyler H. McCormick
As artificial intelligence and machine learning tools become more accessible, and scientists face new obstacles to data collection (e.g. rising costs,…

Comparing the Robustness of Simple Network Scale-Up Method (NSUM) Estimators
Jessica P. Kunke, Ian Laga, Xiaoyue Niu, Tyler H. McCormick
The network scale-up method (NSUM) is a cost-effective approach to estimating the size or prevalence of a group of people that is hard to reach through a…

Robustly estimating heterogeneity in factorial data using Rashomon Partitions
Aparajithan Venkateswaran, Anirudh Sankar, Arun G. Chandrasekhar, Tyler H. McCormick
Many statistical analyses, in both observational data and randomized control trials, ask: how does the outcome of interest vary with combinations of observable…

Bayesian Age Category Reconciliation for Age- and Cause-specific Under-five Mortality Estimates
Shuxian Fan, Li Liu, Jamie Perin, Tyler H. McCormick
Age-disaggregated health data is crucial for effective public health planning and monitoring. Monitoring under-five mortality, for example, requires highly…

Bayesian Active Questionnaire Design for Cause-of-Death Assignment Using Verbal Autopsies
Toshiya Yoshida, Trinity Shuxian Fan, Tyler McCormick, Zhenke Wu, Zehang Richard Li
Only about one-third of the deaths worldwide are assigned a medically-certified cause, and understanding the causes of deaths occurring outside of medical…

Identifying the latent space geometry of network models through analysis of curvature
Shane Lubold, Arun G. Chandrasekhar, Tyler H. McCormick
A common approach to modeling networks assigns each node to a position on a low-dimensional manifold where distance is inversely proportional to connection…

Asymptotically Normal Estimation of Local Latent Network Curvature
Steven Wilkins-Reeves, Tyler McCormick
Network data, commonly used throughout the physical, social, and biological sciences, consists of nodes (individuals) and the edges (interactions) between them…

Bayesian Hyperbolic Multidimensional Scaling
Bolun Liu, Shane Lubold, Adrian E. Raftery, Tyler H. McCormick
Multidimensional scaling (MDS) is a widely used approach to representing high-dimensional, dependent data. MDS works by assigning each observation a location…

Consistently estimating network statistics using Aggregated Relational Data
Emily Breza, Arun G. Chandrasekhar, Shane Lubold, Tyler H. McCormick, Mengjie Pan
Collecting complete network data is expensive, time-consuming, and often infeasible. Aggregated Relational Data (ARD), which capture information about a social…

The openVA Toolkit for Verbal Autopsies
Zehang Richard Li, Jason Thomas, Eungang Choi, Tyler H. McCormick, Samuel J. Clark
Verbal autopsy (VA) is a survey-based tool widely used to infer cause of death (COD) in regions without complete-coverage civil registration and vital…

Regression of exchangeable relational arrays
Frank W. Marrs, Bailey K. Fosdick, Tyler H. McCormick
Relational arrays represent measures of association between pairs of actors, often in varied contexts or over time. Trade flows between countries, financial…

Sequential Estimation of Temporally Evolving Latent Space Network Models
Kathryn Turnbull, Christopher Nemeth, Matthew Nunes, Tyler McCormick
In this article we focus on dynamic network data which describe interactions among a fixed population through time. We model this data using the latent space…

Spectral goodness-of-fit tests for complete and partial network data
Shane Lubold, Bolun Liu, Tyler H. McCormick
Networks describe the, often complex, relationships between individual actors. In this work, we address the question of how to determine whether a parametric…

Inference for Network Regression Models with Community Structure
Mengjie Pan, Tyler H. McCormick, Bailey K. Fosdick
Network regression models, where the outcome comprises the valued edge in a network and the predictors are actor or dyad-level covariates, are used extensively…

The "given data" paradigm undermines both cultures
Tyler McCormick
Breiman organizes "Statistical modeling: The two cultures" around a simple visual. Data, to the far right, are compelled into a "black box" with an arrow and…

Anomaly Detection in Large Scale Networks with Latent Space Models
Wesley Lee, Tyler H. McCormick, Joshua Neil, Cole Sodja, Yanran Cui
We develop a real-time anomaly detection algorithm for directed activity on large, sparse networks. We model the propensity for future activity using a dynamic…

Modeling the social media relationships of Irish politicians using a generalized latent space stochastic blockmodel
Tin Lok James Ng, Thomas Brendan Murphy, Ted Westling, Tyler H. McCormick, Bailey K. Fosdick
Dáil \'Eireann is the principal chamber of the Irish parliament. The 31st Dáil \'Eireann is the principal chamber of the Irish parliament. The 31st Dáil was in…

Estimating spillovers using imprecisely measured networks
Morgan Hardy, Rachel M. Heath, Wesley Lee, Tyler H. McCormick
In many experimental contexts, whether and how network interactions impact the outcome of interest for both treated and untreated individuals are key concerns…

Using Bayesian latent Gaussian graphical models to infer symptom associations in verbal autopsies
Zehang Richard Li, Tyler H. McCormick, Samuel J. Clark
Learning dependence relationships among variables of mixed types provides insights in a variety of scientific settings and is a well-studied problem in…

Bayesian Joint Spike-and-Slab Graphical Lasso
Zehang Richard Li, Tyler H. McCormick, Samuel J. Clark
In this article, we propose a new class of priors for Bayesian inference with multiple Gaussian graphical models. We introduce fully Bayesian treatments of two…

Introducing Bayesian Analysis with $\text{m&m's}^\circledR$: an active-learning exercise for undergraduates
Gwendolyn Eadie, Daniela Huppenkothen, Aaron Springford, Tyler McCormick
We present an active-learning strategy for undergraduates that applies Bayesian analysis to candy-covered chocolate $\text{m&m's}^\circledR$. The exercise…

An Expectation Conditional Maximization approach for Gaussian graphical models
Zehang Richard Li, Tyler H. McCormick
Bayesian graphical models are a useful tool for understanding dependence relationships among many variables, particularly in situations with external prior…

Beyond prediction: A framework for inference with variational approximations in mixture models
Ted Westling, Tyler H. McCormick
Variational inference is a popular method for estimating model parameters and conditional distributions in hierarchical and mixed models, which arise…

Bayesian factor models for probabilistic cause of death assessment with verbal autopsies
Tsuyoshi Kunihama, Zehang Richard Li, Samuel J. Clark, Tyler H. McCormick
The distribution of deaths by cause provides crucial information for public health planning, response, and evaluation. About 60% of deaths globally are not…

Using Aggregated Relational Data to feasibly identify network structure without network data
Emily Breza, Arun G. Chandrasekhar, Tyler H. McCormick, Mengjie Pan
Social network data is often prohibitively expensive to collect, limiting empirical network research. Typical economic network mapping requires (1) enumerating…

Multiresolution network models
Bailey K. Fosdick, Tyler H. McCormick, Thomas Brendan Murphy, Tin Lok James Ng, Ted Westling
Many existing statistical and machine learning tools for social network analysis focus on a single level of analysis. Methods designed for clustering optimize…

Modeling Recovery Curves With Application to Prostatectomy
Fulton Wang, Tyler H. McCormick, Cynthia Rudin, John Gore
We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the…

Inferring social structure from continuous-time interaction data
Wesley Lee, Bailey K. Fosdick, Tyler H. McCormick
Relational event data, which consist of events involving pairs of actors over time, are now commonly available at the finest of temporal resolutions. Existing…

Hyak Mortality Monitoring System: Innovative Sampling and Estimation Methods - Proof of Concept by Simulation
Samuel J. Clark, Jon Wakefield, Tyler McCormick, Michelle Ross
Traditionally health statistics are derived from civil and/or vital registration. Civil registration in low-income countries varies from partial coverage to…

Redrawing the 'Color Line': Examining Racial Segregation in Associative Networks on Twitter
Nina Cesare, Hedwig Lee, Tyler McCormick, Emma S. Spiro
Online social spaces are increasingly salient contexts for associative tie formation. However, the racial composition of associative networks within most of…

Estimating population size using the network scale up method
Rachael Maltiel, Adrian E. Raftery, Tyler H. McCormick, Aaron J. Baraff
We develop methods for estimating the size of hard-to-reach populations from data collected using network-based questions on standard surveys. Such data arise…

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan
We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a…

Probabilistic Cause-of-death Assignment using Verbal Autopsies
Tyler H. McCormick, Zehang Li, Clara Calvert, Amelia C. Crampin, Kathleen Kahn, Samuel J. Clark
In regions without complete-coverage civil registration and vital statistics systems there is uncertainty about even the most basic demographic indicators. In…

Reactive point processes: A new approach to predicting power failures in underground electrical systems
Åžeyda Ertekin, Cynthia Rudin, Tyler H. McCormick
Reactive point processes (RPPs) are a new statistical model designed for predicting discrete events in time based on past history. RPPs were developed to…

InSilicoVA: A Method to Automate Cause of Death Assignment for Verbal Autopsy
Samuel J. Clark, Tyler McCormick, Zehang Li, Jon Wakefield
Verbal autopsies (VA) are widely used to provide cause-specific mortality estimates in developing world settings where vital registration does not function…

Clustering South African households based on their asset status using latent variable models
Damien McParland, Isobel Claire Gormley, Tyler H. McCormick, Samuel J. Clark, Chodziwadziwa Whiteson Kabudula, Mark A. Collinson
The Agincourt Health and Demographic Surveillance System has since 2001 conducted a biannual household asset survey in order to quantify household socio…

Latent demographic profile estimation in hard-to-reach groups
Tyler H. McCormick, Tian Zheng
The sampling frame in most social science surveys excludes members of certain groups, known as hard-to-reach groups. These groups, or subpopulations, may be…

Bayesian hierarchical rule modeling for predicting medical conditions
Tyler H. McCormick, Cynthia Rudin, David Madigan
We propose a statistical modeling technique, called the Hierarchical Association Rule Model (HARM), that predicts a patient's possible future medical…