The 2018 Michigan State Symposium on mathematical statistics and applications was held at MSU from
Sep 14-16, 2018. It was a conference designed around the scientific legacy of Prof. Hira L. Koul, who was a
member of MSU’s department of statistics and probability for decades. The scientific areas
represented at the conference are all connected, often quite directly, to the work which Prof. Koul has produced.
Topics covered included : Semi- and non-parametric foundations of data science; Asymptotic theory of efficient
and adaptive estimation; Inference for high-dimensional data; Inference for long-memory and other stochastic processes;
Nonlinear Time Series analysis with applications to econometrics and finance; Robust multivariate methods; Survival
analysis and its applications; and Sequential estimation and design. With ample time built into the schedule for discussions,
the conference gave participants opportunities to engage in emerging and fruitful cross-group collaborations.
It brought together established and aspiring researchers from around the country and abroad, to explore frontiers of
mathematical statistics.
Date: September 14-16, 2018
Location: Breslin Student Events Center, Michigan State University
Sponsors
National Science Foundation
Institute of Mathematical Statistics
Special Thanks
We would like to extend special thanks to these individuals for their generous donation in support of the Symposium on Mathematical Statistics and Applications:
- Tao He
- Linyuan Li
- Xiaoqing Zhu
Plenary Speakers
- Richard Davis (Columbia University)
- Phil Duxbury (Michigan State University)
- Philip Ernst (Rice University)
- Jianqing Fan (Princeton University)
- Joseph Gardiner (Michigan State University)
- Tailen Hsing (University of Michigan)
- Richard Johnson (University of Wisconsin, Madison)
- S.N. Lahiri (North Carolina State University)
- Regina Liu (Rutgers University)
- Ursula Mueller (Texas A&M University)
- Lianfen Qian (Florida Atlantic University)
- Annie Qu (University of Illinois at Urbana-Champaign)
- Anton Schick (SUNY Binghamton)
- Donatas Surgailis (Vilnius University, Lithuania)
Symposium Topics
- Semi- and non-parametric foundations of data science
- Asymptotic theory of efficient and adaptive estimation
- Inference for high-dimensional data
- Inference for time series, long-memory stochastic processes, and applications to econometrics and finance
- Survival analysis and its applications
- Statistical properties of machine-learning algorithms.