“A ‘Divide-and-Conquer’ AECM Algorithm for Large non-Gaussian Longitudinal Data with Irregular Follow-Ups, presented by Reuben Retnam, Takeda Pharmaceuticals
Events
Statistics Colloquium: Scott Bruce, Texas A&M University
“Interpretable Classification of Categorical Time Series Using the Spectral Envelope and Optimal Scalings,” presented by Scott Bruce, Assistant Professor, Department of Statistics, Texas A & M University
Paper of the Month: September 2022
Belkin, M., Hsu, D., Ma, S., & Mandal, S. (2019). Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences, 116(32), 15849-15854.
Interdisciplinary Seminar: Kosuke Imai, Harvard University
“Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment,” presented by Kosuke Imai, Professor of Government and of Statistics, Harvard University, Institute for Quantitative Social Science.
Statistics Colloquium: Jae-Kwang Kim, Iowa State University
“Multiple Bias Calibration for Valid Statistical Inference With Selection Bias,” presented by Jae-Kwang Kim, LAS Dean’s Professor, Department of Statistics, Iowa State University.
UConn Department of Statistics – Alumni Survey
Alumni are invited to complete a survey in honor of the Department of Statistics 60th Anniversary Celebration in October, 2022.
Interdisciplinary Seminar: Luke Keele, University of Pennsylvania
“Combining Experimental and Population Data to Estimate Population Treatment Effects,” presented by Dr. Elizabeth Stuart, Johns Hopkins Bloomberg School of Public Health
Statistics Colloquium: Yifei Sun, Columbia University
“Dynamic Risk Prediction Triggered by Intermediate events Using Survival Tree Ensembles,” presented by Yifei Sun, Assistant Professor, Department of Biostatistics, Columbia University
Statistics Colloquium: Chuan-Fa Tang, University of Texas at Dallas
“Taylor’s Law for Semivariance and Higher Moments of Heavy-Tailed Distributions,” presented by Chuan-Fa Tang, Assistant Professor, Department of Mathematical Sciences, University of Texas at Dallas
Statistics Colloquium: Scott Linderman, Stanford University
“Point Process Models for Sequence Detection in Neural Spike Trains,” presented by Scott Linderman, Assistant Professor, Department of Statistics, Stanford University