Statistics and Data Science Seminar - Fall 2019

Usually meets Wednesday at 4:00 pm in SEO 636.
August 28
(4:00 pm; 636 SEO)
Yichao Wu
Organizational meeting
September 4
(4:00 pm; 636 SEO)
Zhihua Su
University of Florida
Envelope-based Sparse Partial Least Squares
September 11
(4:00 pm; 636 SEO)
Stacey Tannenbaum
Astellas Pharma
Pharmacometrics: Application of MSCS to Pharmaceutical Development
September 18
(4:00 pm; 636 SEO)
Jun Li
University of Notre Dame
A sparse clustering algorithm for identifying cluster changes across conditions with applications in single-cell RNA-sequencing data
September 25
(4:00 pm; 636 SEO)
Dan Nettleton
Iowa State University
Random Forest Prediction Intervals
October 2
(4:00 pm; 636 SEO)
Ning Hao
University of Arizona
Equivariant Variance estimation for multiple change-point model
October 9
(3:00 pm; 636 SEO)
Jianfeng Zhang
A Martingale Approach for Fractional Brownian Motions and Related Path Dependent PDEs
October 9
(4:00 pm; 636 SEO)
Hira Koul
Michigan State University
Weighted empirical minimum distance estimators in Berkson measurement error regression models
October 16
(4:00 pm; 636 SEO)
Yixin Fang
AbbVie Inc.
A statistical roadmap for journey from real-world data to real-world evidence
October 23
(4:00 pm; 636 SEO)
Xianyang Zhang
Texas A&M University
A New Framework for Distance and Kernel-based Metrics in High Dimensions
October 30
(4:00 pm; 636 SEO)
Minge Xie
Rutgers University
Repro Sampling Method for Joint Inference of Model Selection and Regression Coefficients in High Dimensional Linear Models
November 6
(4:00 pm; 636 SEO)
Dr. Ching Jin
Northwestern University
Understanding Dynamical Patterns in Complex Substitutive Systems
November 13
(4:00 pm; 636 SEO)
Jennifer Pajda-Delao
Unbiased Estimation and Median-Unbiasedness in Finite Population Survey Sampling
November 20
(4:00 pm; 636 SEO)
Xiangrong Yin
University of Kentucky
Moment Kernel for Estimating Central Mean Subspace and Central Subspace
November 27
December 4
(4:15 pm; 636 SEO)
Seonghyun Jeong
University of Chicago
Bayesian high-dimensional logit models: categorical responses and group sparsity
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