Statistical Inference for Population Substructures via Clustering, Mixture Models and other Approaches Session Organizers: Yongzhao Shao, New York University Demissie Alemayehu, Pfizer
Inference about population
substructures arises naturally in many studies across a variety of
disciplines including biology, economics, engineering, and medicine.
Various statistical methods and models have been developed over the
last few decades. However, despite extensive research by many
statisticians, there have been many important theoretical questions and
problems concerning related inferential issues (e.g. use of likelihood
ratio test) challenging the statistical community for satisfactory
answers. This invited session includes three lectures that discuss
theoretical challenges and progresses in clustering, mixture models and
other approaches.
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