Tyler H. McCormick

Tyler H. McCormick
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Tyler H. McCormick
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Statistics - Applications (12)
 
Statistics - Methodology (8)
 
Statistics - Machine Learning (2)
 
Computer Science - Learning (1)

Publications Authored By Tyler H. McCormick

Social and economic network data can be useful for both researchers and policymakers, but can often be impractical to collect. We propose collecting Aggregated Relational Data (ARD) using questions that are simple and easy to add to any survey. These question are of the form "how many of your friends in the village have trait k?" We show that by collecting ARD on even a small share of the population, researchers can recover the likely distribution of statistics from the underlying network. Read More

Relational arrays represent interactions or associations between pairs of actors, often over time or in varied contexts. We focus on the case where the elements of a relational array are modeled as a linear function of observable covariates. Due to the inherent dependencies among relations involving the same individual, standard regression methods for quantifying uncertainty in the regression coefficients for independent data are invalid. Read More

Relational event data, which consist of events involving pairs of actors over time, are now commonly available at the finest of temporal resolutions. Existing continuous-time methods for modeling such data are based on point processes and directly model interaction "contagion," whereby one interaction increases the propensity of future interactions among actors, often as dictated by some latent variable structure. In this article, we present an alternative approach to using temporal-relational point process models for continuous-time event data. Read More

Social networks exhibit two key topological features: global sparsity and local density. That is, the overall propensity for interaction between any two randomly selected actors is infinitesimal, but for any given individual there is massive variability in the propensity to interact with others in the network. Further, relevant scientific questions typically depending on the scale of analysis. Read More

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 series of if.. Read More

Variational inference is a popular method for estimating model parameters and conditional distributions in hierarchical and mixed models, which arise frequently in many applied settings in the health, social and biological sciences. Variational inference for these models is typically less computationally intensive than maximum likelihood or posterior-based estimation, enabling scientists to fit rich models even with extremely large datasets. Despite widespread use, little is known about the general theoretical properties of variational estimators, which hinders their use in inferential statistics. Read More

Reactive point processes (RPPs) are a new statistical model designed for predicting discrete events in time based on past history. RPPs were developed to handle an important problem within the domain of electrical grid reliability: short-term prediction of electrical grid failures ("manhole events"), including outages, fires, explosions and smoking manholes, which can cause threats to public safety and reliability of electrical service in cities. RPPs incorporate self-exciting, self-regulating and saturating components. Read More

We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery. We illustrate the utility of our model as a pre-treatment medical decision aid, producing personalized predictions that are both interpretable and accurate. Read More

Traditionally health statistics are derived from civil registration and vital statistics (CRVS). CRVS in low- to middle-income countries varies from partial coverage to essentially nothing at all. Consequently the state of the art for public health information in low- to middle-income countries is efforts to combine or triangulate data from different sources to produce a more complete picture across both time and space - what we term 'data melding'. Read More

Verbal autopsies (VA) are widely used to provide cause-specific mortality estimates in developing world settings where vital registration does not function well. VAs assign cause(s) to a death by using information describing the events leading up to the death, provided by care givers. Typically physicians read VA interviews and assign causes using their expert knowledge. Read More

In regions without complete-coverage civil registration and vital statistics systems there is uncertainty about even the most basic demographic indicators. In such areas the majority of deaths occur outside hospitals and are not recorded. Worldwide, fewer than one-third of deaths are assigned a cause, with the least information available from the most impoverished nations. Read More

The Agincourt Health and Demographic Surveillance System has since 2001 conducted a biannual household asset survey in order to quantify household socio-economic status (SES) in a rural population living in northeast South Africa. The survey contains binary, ordinal and nominal items. In the absence of income or expenditure data, the SES landscape in the study population is explored and described by clustering the households into homogeneous groups based on their asset status. Read More

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 by asking respondents how many people they know in a specific group (e.g. Read More

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 difficult to access (the homeless, e.g. Read More

We propose a statistical modeling technique, called the Hierarchical Association Rule Model (HARM), that predicts a patient's possible future medical conditions given the patient's current and past history of reported conditions. The core of our technique is a Bayesian hierarchical model for selecting predictive association rules (such as "condition 1 and condition 2 $\rightarrow$ condition 3") from a large set of candidate rules. Because this method "borrows strength" using the conditions of many similar patients, it is able to provide predictions specialized to any given patient, even when little information about the patient's history of conditions is available. Read More