报告主题：Probabilistic exponential family inverse regression and its applications
时间：2024年10月30日15：00-15：40
地点：上川路校区一教218
报告人：王涛
报告内容简介：
Rapid
advances in high-throughput sequencing technologies have led to the fast
accumulation of high-dimensional data, which is harnessed for understanding the
implications of various factors on human disease and health. While dimension
reduction plays an essential role in high-dimensional regression and
classification, existing methods often require the predictors to be continuous,
making them unsuitable for discrete data, such as presence-absence records of
species in community ecology and sequencing reads in single-cell studies. To
identify and estimate sufficient reductions in regressions with discrete
predictors, we introduce probabilistic exponential family inverse regression
(PrEFIR), assuming that, given the response and a set of latent factors, the
predictors follow one-parameter exponential families. We show that the
low-dimensional reductions result not only from the response variable but also
from the latent factors. We further extend the latent factor modeling framework
to the double exponential family by including an additional parameter to
account for the dispersion. This versatile framework encompasses regressions
with all categorical or a mixture of categorical and continuous predictors. We
propose the method of maximum hierarchical likelihood for estimation, and
develop a highly parallelizable algorithm for its computation. The
effectiveness of PrEFIR is demonstrated through simulation studies and real
data examples.
主讲人简介：
王涛博士是上海交通大学教授、博士生导师，交大-耶鲁生物统计与数据科学联合中心研究员。他曾在美国耶鲁大学生物统计系进行博士后研究。主要研究领域为生物医学大数据的统计共性算法和理论。获得国家自然科学基金优秀青年科学基金项目资助，并受邀担任PLOS Computational Biology等权威期刊编委。他还积极参与了教育部生物科学“101”计划生物信息学核心课程的建设和核心教材的编写工作。