# Tyler Mccormick

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#### Contact

#### Research Interests

## Bio

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

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What's the Weight? Estimating Controlled Outcome Differences in Complex Surveys for Health Disparities Research
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Stephen Salerno, Emily K. Roberts, Belinda L. Needham, Tyler H. McCormick, Bhramar Mukherjee, Xu Shi
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A basic descriptive question in statistics often asks whether there are differences in mean outcomes between groups based on levels of a discrete covariate (e…

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Some models are useful, but for how long?: A decision theoretic approach to choosing when to refit large-scale prediction models
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Kentaro Hoffman, Stephen Salerno, Jeff Leek, Tyler McCormick
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Large-scale prediction models (typically using tools from artificial intelligence, AI, or machine learning, ML) are increasingly ubiquitous across a variety of…

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Estimating and Correcting Degree Ratio Bias in the Network Scale-up Method
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Ian Laga, Jessica P. Kunke, Tyler H. McCormick, Xiaoyue Niu
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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…

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

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Feasible contact tracing
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Aparajithan Venkateswaran, Jishnu Das, Tyler H. McCormick
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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…

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Model-Based Inference and Experimental Design for Interference Using Partial Network Data
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Steven Wilkins Reeves, Shane Lubold, Arun G. Chandrasekhar, Tyler H. McCormick
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The stable unit treatment value assumption states that the outcome of an individual is not affected by the treatment statuses of others, however in many real…

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

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From Narratives to Numbers: Valid Inference Using Language Model Predictions from Verbal Autopsy Narratives
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Shuxian Fan, Adam Visokay, Kentaro Hoffman, Stephen Salerno, Li Liu, Jeffrey T. Leek, Tyler H. McCormick
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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…

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Data-adaptive exposure thresholds for the Horvitz-Thompson estimator of the Average Treatment Effect in experiments with network interference
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Vydhourie Thiyageswaran, Tyler McCormick, Jennifer Brennan
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Randomized controlled trials often suffer from interference, a violation of the Stable Unit Treatment Values Assumption (SUTVA) in which a unit's treatment…

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Dempster-Shafer P-values: Thoughts on an Alternative Approach for Multinomial Inference
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Kentaro Hoffman, Kai Zhang, Tyler McCormick, Jan Hannig
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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…

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

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

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Comparing the Robustness of Simple Network Scale-Up Method (NSUM) Estimators
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Jessica P. Kunke, Ian Laga, Xiaoyue Niu, Tyler H. McCormick
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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…

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

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

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

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

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

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

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The openVA Toolkit for Verbal Autopsies
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Zehang Richard Li, Jason Thomas, Eungang Choi, Tyler H. McCormick, Samuel J. Clark
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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…

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

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Sequential Estimation of Temporally Evolving Latent Space Network Models
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Kathryn Turnbull, Christopher Nemeth, Matthew Nunes, Tyler McCormick
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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…

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

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Inference for Network Regression Models with Community Structure
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Mengjie Pan, Tyler H. McCormick, Bailey K. Fosdick
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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…

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The "given data" paradigm undermines both cultures
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Tyler McCormick
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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…

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Anomaly Detection in Large Scale Networks with Latent Space Models
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Wesley Lee, Tyler H. McCormick, Joshua Neil, Cole Sodja, Yanran Cui
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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…

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Modeling the social media relationships of Irish politicians using a generalized latent space stochastic blockmodel
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Tin Lok James Ng, Thomas Brendan Murphy, Ted Westling, Tyler H. McCormick, Bailey K. Fosdick
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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…

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

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

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Bayesian Joint Spike-and-Slab Graphical Lasso
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Zehang Richard Li, Tyler H. McCormick, Samuel J. Clark
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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…

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

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

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

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

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

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

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

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

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

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

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Estimating population size using the network scale up method
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Rachael Maltiel, Adrian E. Raftery, Tyler H. McCormick, Aaron J. Baraff
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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…

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Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
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Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan
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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…

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

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

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

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

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Latent demographic profile estimation in hard-to-reach groups
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Tyler H. McCormick, Tian Zheng
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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…

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