Department of Statistics
Environmental Seminar Series
North Carolina State University


Bo Li

FROM  Department of Statistics

Of University of Illinois at Urbana-Champaign


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 autoregressive

(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

Thursday,2 November, 2017
4:30-5:30 pm

315 Riddick Hall

Refreshments will be served in the 5th floor commons at 4:00 pm.
NOTE: No food or drink is allowed in any of the classrooms in SAS Hall.