Neyman Seminar

The Neyman seminar is the statistics seminar in the Department. Historically, it has been focused on applications of Statistics to other fields. Nowadays, it has a very broad scope, with topics ranging from applications of statistics to theory.

The seminar is held on Wednesdays from 4 to 5 in the Jerzy Neyman room, 1011 Evans.

Details of individual seminar events are published in the campus' event system.

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Recent & Upcoming Neyman Seminars

Sensitivity Analysis in Observational Research: Introducing the E-Value
Tyler VanderWeele, Harvard School of Public Health
Nov 26, 2018 12:00pm
1011 Evans Hall
Abstract:
Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the “E-value,” which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an...
Support points – a new way to reduce big and high-dimensional data
Simon Mak, Georgia Institute of Technology
Jan 28, 2019 4:00pm
1011 Evans Hall
Abstract:
This talk presents a new method for reducing big and high-dimensional data into a smaller dataset, called support points (SPs). In an era where data is plentiful but downstream analysis is oftentimes expensive, SPs can be used to tackle many big data challenges in statistics, engineering and machine learning. SPs have two key advantages over existing methods. First, SPs provide optimal and...
The Stratified Micro-randomized Trial Design: Sample Size Considerations for Testing Nested Causal Effects of Time-varying Treatment
Walter Dempsey, Harvard University
Jan 30, 2019 4:00pm
1011 Evans Hall
Abstract:
Technological advancements in the field of mobile devices and wearable sensors have helped overcome obstacles in the delivery of care, making it possible to deliver behavioral treatments anytime and anywhere. Delivery of these treatments is increasingly triggered by detections/predictions of vulnerability and receptivity, which may have been impacted by prior treatments. Furthermore the...
Statistical inference for infectious disease modeling
Po-ling Loh, University of Wisconsin-Madison
Feb 4, 2019 4:00pm
1011 Evans Hall
Abstract:
We discuss two recent results concerning disease modeling on networks. The infection is assumed to spread via contagion (e.g., transmission over the edges of an underlying network). In the first scenario, we observe the infection status of individuals at a particular time instance and the goal is to identify a confidence set of nodes that contain the source of the infection with high probability....
Statistics on Shape Data: Correcting an Asymptotic Bias in Template Shape Estimation
Nina Miolane, Stanford University
Feb 20, 2019 4:00pm
1011 Evans Hall
Abstract:
Computational Anatomy aims to model and analyze healthy and pathological distributions of organ shapes. We are interested in the computational representation of the brain anatomy using brain MRIs (Magnetic Resonance Imaging). How can we define the notion of brain shapes and how can we learn their distribution in the population? Landmarks’ shapes, curve shapes or surface shapes can be seen as the...
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