| 主讲人 |
Jiahua Chen |
简介 |
<p> <span lang="EN-US" style="font-size: 10.5pt; font-family: 'Times New Roman', serif;">In scientific investigations, a population is often suspected of containing several more homogeneous sub-populations. Such a population structure is most accurately described by a finite mixture model. The evidence for mixture is best examined through a rigorous statistical hypothesis test. Developing valid and effective statistical inference methods is an important and challenging research problem. Classical procedures when applied to mixture models often have sophisticated asymptotic properties which render them useless in applications. For many finite mixture models, we have successfully designed corresponding EM-tests whose limiting distributions are easier to derive mathematically and simpler for implementation in data analysis. This talk illustrates the ideas behind the EM-tests, their elegant asymptotic properties and other related issues.</span></p> |
