Upcoming Events For Statistics Seminar

Tue Feb 27, 2018

ACMS Statistics Seminar: Won Chang

3:30 PM - 4:30 PM
154 Hurley Hall

Won Chang
University of Cincinnati

3:30 PM
154 Hurley Hall

Computer Model Emulation and Calibration using High-dimensional and Non-Gaussian Spatial Data

I will introduce statistical methods to calibrate complex computer models using high-dimensional spatial data sets. This work is motivated by important research problems in climate science where such computer models are frequently used. Computer models play a central role in generating projections of future climate. An important source of uncertainty about future projections from these models is due to uncertainty about input parameters that are key drives of the resulting hindcasts and projections. Computer model calibration is a statistical framework for inferring the input parameters by combining information from computer model runs and observational data. When the data are in the form of high-dimensional spatial fields, computer model emulation (approximation) and calibration can pose significant inferential and computational challenges. The goal of this research is to develop new approaches to computer model calibration that are computationally efficient, accurate, and carefully account for uncertainties.…

Posted In: Statistics Seminar

Tue Mar 20, 2018

ACMS Statistics Seminar: Bo Li

3:30 PM - 4:30 PM
154 Hurley Hall

Bo Li
UIUC

3:30 PM
154 Hurley Hall

Spatially Varying Autoregressive Models for Prediction of New HIV Diagnoses

In demand of predicting new HIV diagnosis rates based on publicly available HIV data that is abundant in space but has few points in time, we propose a class of spatially varying autoregressive (SVAR) models compounded with conditional au- toregressive (CAR) spatial correlation structures. We then propose to use the copula approach and a flexible CAR formulation to model the dependence between adjacent counties. These models allow for spatial and temporal correlation as well as space-time interactions and are naturally suitable for predicting HIV cases and other spatio-temporal disease data that feature a similar data structure. We apply the proposed models to HIV data over Florida, California and New England states and compare them to a range of linear mixed models that have been recently popular for modeling spatio-temporal disease data. The results show that for such data our proposed models outperform the others in terms of prediction.…

Posted In: Statistics Seminar