SAV

Building Map

Estimating and Forecasting the Smoking-attributable Mortality Fraction for Both Sexes Jointly in 69 Countries

Time
Speaker
Yicheng Li

Smoking is one of the preventable threats to human health and is a major risk factor for lung cancer, upper aero-digestive cancer, and chronic obstructive pulmonary disease. Estimating and forecasting the smoking attributable fraction (SAF) of mortality can yield insights into smoking epidemics and also provide a basis for more accurate mortality and life expectancy projection.

Building
Room
409

Statistical Inference for the Mean Outcome Under a Possibly Non-Unique Optimal Treatment Strategy

Time
Speaker
Alex Luedtke

An individualized treatment rule (ITR) is a treatment rule which assigns treatments to individuals based on (a subset of) their measured covariates. An optimal ITR is the ITR which maximizes the population mean outcome. In any given problem, there is no guarantee that the optimal ITR will outperform standard practice. The utility of personalization can be explored using a confidence interval for the mean outcome under the optimal rule.

Building
Room
409

Joint Modeling for Longitudinal and Time-To-Event Data: An Application in Nephrology

Time
Speaker
Theresa R. Smith

Many medical studies collect both repeated measures data and survival data. In this talk, I discuss jointly modeling these two kinds of data in a study of patients with chronic kidney disease in which longitudinal biomarkers of kidney function and time to cardiovascular events were recorded. Joint modeling these processes is important because the measurements of kidney function are error-prone. Ignoring this error (e.g., using a simpler time-varying covariates model) can give biased estimates of the effect of kidney function on the risk of cardiovascular events.

Building
Room
409

Middles: Means, Medians, Metrics, and Other Things That Start With M

Time
Speaker
J. McLean Sloughter

One of the first topics to come up in an introductory statistics course is means and medians. But why do we have more than one way of measuring the "middle" of a set of data? This talk will show how different metrics (ways of defining distance) give rise to different measures of "middle", as well as looking at some of the practical reasons we might pick one measure over another.

Building
Room
409

Linear Structural Equation Models with Non-Gaussian Errors

Time
Speaker
Yu-Hsuan S. Wang

In this talk, we consider structural equation models represented by a mixed graph which encode both direct causal relationships as well as latent confounding. First, we use an empirical likelihood approach to fit structural equation models without explicitly assuming a distributional form for the errors. Through simulations, we show that when the errors are skewed, the empirical likelihood approach may provide a more efficient estimator than methods assuming a Gaussian likelihood.

Building
Room
140

Scalable Manifold Learning

Time
Speaker
James Michael McQueen

Advisor: Marina Meila Abstract: This talk investigates the methodology and scalability of non-linear dimension reduction techniques. With data being observed in increasingly higher dimensions and on a larger scale than before, the demand for non-linear dimension reduction is growing. There is very little consensus, however, on how non-linear dimension reduction should be performed. The goal of Manifold Learning (ML) is to embed the data into s-dimensional Euclidean space (where manifold dimension < s < observed dimension) without distorting the geometry. Existing ML algorithms (e.g.

Building
Room
409

Lord's Paradox and Targeted Interventions: The Case of Special Education

Time
Speaker
Roderick M. Theobald

Advisors: Thomas Richardson and Dan Goldhaber Lord (1967) describes a hypothetical “paradox” in which two statisticians, analyzing the same dataset using different but defensible methods, come to very different conclusions about the effects of an intervention on student outcomes.

Building
Room
409

Shaping Social Activity by Incentivizing Users

Time
Speaker
Le Song

Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state?

Building
Room
264

Statistical Machine Learning and Big-p Data

Time
Speaker
Pradeep Ravikumar

With modern \"Big Data\" settings, off-the-shelf statistical machine learning methods are frequently proving insufficient. A key challenge posed by these modern settings is that the data might have a large number of features, in what we will call \"Big-p\" data, to denote the fact that the dimension \"p\" of the data is large, potentially even larger than the number of samples.

Building
Room
409

A nonparametric Bayesian model for legislative voting

Time
Speaker
Abel Rodriguez

Legislative voting records are widely used in political sciences to characterize revealed preferences among the member of a deliberative assembly. In this context, item-response models (a class of latent factor models) such as NOMINATE and IDEAL are the preeminent quantititive tools used for analysis. This class of models assumes that member\'s choices can be explained by continuous latent features, often called ideal points. For unidimensional latent spaces, this often results in a ranking of members along the liberal-conservative spectrum.

Building
Room
260