Statistical Inference for Population Substructures
via Clustering, Mixture Models and other Approaches
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.