时间：
7月21日 10:30-11:30
个人简介：
Chuanhai Liu earned his master's
degree in Probability and Statistics from Wuhan University in 1987 and his PhD
in Statistics from Harvard University in 1994. He worked at Bell Laboratories
for ten years starting in 1995 and at Texas A&M as an Associate Professor
in Spring 2004. Since 2005, he has been a Professor of Statistics at Purdue
University. His research interests include the foundations of statistical
inference, statistical computing, and applied statistics. Much of his work on
iterative algorithms, such as Quasi-Newton, EM, and MCMC methods, is discussed
in his book titled "Advanced Markov Chain Monte Carlo Methods"
(2010), co-authored with F. Liang and R. J. Carroll. His work on the
foundations of statistical inference, developing a new inferential framework
for prior-free probabilistic inference, is included in his book titled
"Inferential Models: Reasoning with Uncertainty" (2015), co-authored
with R. Martin. For his research on statistical computing, he spent several
years experimenting with a multi-threaded and distributed R software system
called SupR for big data analysis. Currently, he is working on topics for a new
book titled "Scientific Modeling: : Principles, Methods and
Examples."
报告摘要：
This era of big data is fascinating for data
analysis in particular and statistics in general. It has also clearly revealed
more than ever different scientific attitudes toward data analysis and
statistical research from different perspectives. As statisticians, we see both
challenges and responsibility for foundational developments in both statistical
inference and scientific modeling. This talk introduces a new principle, called
the prediction principle. We argue that this principle can serve as a first principle
for valid and efficient inference by exploring its implications in three key
research directions: (a) how the prediction principle can be used to refine
both the principle of maximum likelihood and the likelihood principle, (b) how
statistical inference should be formalized, as the required reasoning is
deductive, and (c) how a general theory of scientific modeling might be
achievable, despite the inherent challenges of inductive reasoning. These
discussions are illustrated using seemingly simple but unsolved problems in
high-dimensional statistics and deep learning models. To prompt deeper
reflections, the talk concludes with a few challenging problems.
