The Statistics Graduate Student Association (SGSA) hosted a spring BBQ to show their appreciation to the faculty and staff of the Statistics Department on Saturday, April 22, 2017 at the Brian Bachmann Community Park in College Station.
Special thanks to the SGSA Officers Amir Nikooienjad (President), Tianying Wang (Vice President), Marcin Jurek (Treasurer), Eli Kravitz (Secretary/Social Chair), Alex Lapanowski (Web Designer), Shubhadeep Chakraborty (GPSC Delegate) as well as the entire student body for hosting this event.
11:30 AM / 12:30 PM Blocker Building (BLOC), Room 113 979-845-3141
SUHASINI SUBBA RAO
Department of Statistics
Texas A&M University
Linear Regression with Time Series Regressors
In several diverse applications, from the neurosciences to econometrics, it is of interest to model the influence observed regressors have on a response of interest. In many of these applications, the regressors have a meaningful ordering and are usually a long time series. The problem of linear regression, where the number of regressors n is of the same order or magnitudes larger than the number of responses p has received considerable attention. However, most of these approaches place a sparsity assumption on regressor coefficients. When the regressors are a time series, the sparse assumption can be unrealistic with no intuitive interpretation.
In this talk we consider the problem of linear regression with time series regressors, but work under the assumption that the regressor coefficients are absolutely summable. We propose a computationally efficient method for estimating the regression parameters, that avoids large matrix estimation and inversion. We show that the parameter estimators are consistent and derive a central limit theorem. The proposed estimation scheme, leads to a simple method for estimating the variance of the parameter estimators. Though consistent the parameter estimators are noisy, thus we describe a post-processing step to reduce the noise in the estimators.
Friday, 3/23/2018, 11:30 AM, BLOC 113
03:00 PM / 04:30 PM Blocker Building (BLOC), Room 457 979-845-3141
ERIC B. LABER
Associate Professor of Statistics
North Carolina State University
Recipient of the 2017 Raymond Carroll Young Investigator Award
Optimal Treatment Allocations in Space and Time for Online Control of an Emerging Infectious Disease
A key component in controlling the spread of an epidemic is deciding where, when, and to whom to apply an intervention. We develop a framework for using data to inform these decisions in real-time. We formalize a treatment allocation strategy as a sequence of functions, one per treatment period, that map up-to-date information on the spread of an infectious disease to a subset of locations where treatment should be allocated. An optimal allocation strategy optimizes some cumulative outcome, e.g., the number of uninfected locations, the geographic footprint of the disease, or the cost of the epidemic. Estimation of an optimal allocation strategy for an emerging infectious disease is challenging because spatial proximity induces interference among locations, the number of possible allocations is exponential in the number of locations, and because disease dynamics and intervention effectiveness are unknown at outbreak. We derive a Bayesian online estimator of the optimal allocation strategy that combines simulation-optimization with Thompson sampling. The proposed estimator performs favorably in simulation experiments. This work is motivated by and illustrated using data on the spread of white-nose syndrome, a highly fatal infectious disease devastating bat populations in North America.
Monday, 3/25/2018, 3:00 PM, BLOC 457
11:30 AM / 12:30 PM 979-845-3141