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Tags: UGA Clemson

Bayesian and machine learning frameworks for studying climate anomalies and social conflicts Abstract Climate change stands to have a profound impact on human society, and on political and other conflicts in particular. However, the existing literature on understanding the relation between climate change and societal conflicts has often been criticized for using data that suffer from sampling and other biases, often resulting from being too…
Do statisticians have labs? Introduction to the Precision Medicine Artificial Intelligence Lab (PHAIR) Event Slides Abstract: In this talk, I discuss a non-traditional approach to statistics training through the use of a virtual lab structure. For over a decade, the Precision Health Artificial Intelligence lab gradually evolved from an informal meeting for a few students engaged in similar research topics to a large virtual lab with about 30…
Biography Dr. Dennis K. J. Lin is a University Distinguished Professor and Head of the Statistics Department at Purdue University. His research interests are quality assurance, industrial statistics, data mining, and data science. He has published near 250 SCI/SSCI papers in a wide variety of journals. He currently serves or has served as associate editor for more than 10 professional journals and was co-editor for Applied Stochastic Models for…
On Some New Classes of Disease Mapping Models Hierarchical models for regionally aggregated disease incidence data commonly involve region specific latent random effects that are modelled jointly as having a multivariate Gaussian distribution. The covariance or precision matrix incorporates the spatial dependence between the regions. Common choices for the precision matrix include the widely used intrinsic conditional autoregressive model, which…
Past/History UGA/Clemson Joint Seminars
Heavy Tails and Financial Time Series Models
Motivation and Convergence of Two "New" Fast Algorithms for Estimating the Mixing Distribution in Mixture Models
Ultrahigh Dimensional Variable Selection: Beyond the Linear Model
Object Oriented Data Analysis
BIG Statistics

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