Seminar

2023 Apr 18

Stat 303 Grand Finale Lecture: Professor Bharat N. Anand

3:00pm to 4:30pm

Location: 

316 Science Center

Speaker:  Bharat N. Anand, Vice Provost for Advances in Learning at Harvard University and the Henry R. Byers Professor of Business Administration at Harvard Business School

Title: “The Future of Education: Reflections from the Front Lines”

Abstract: In this talk I will offer a perspective on how, and why, digital technologies have shaped pedagogy and the practice of teaching over the last decade at Harvard, and what this means for the future. Specifically, I will examine: Where...

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2022 May 19

Statistics Seminar with Art Owen

12:00pm to 1:00pm

Location: 

Science Center, Room 316

Please join us for our upcoming Statistics Seminar on May 19th with Art B. Owen who is a Max H. Stein Professor of Statistics at Stanford University.

Title: Tie-Breaker Designs

Abstract: Companies may offer incentives to their best customers and philanthropists may offer scholarships to the strongest students.  They can evaluate the impact of these treatments later using a regression discontinuity analysis. Unfortunately, regression discontinuity analyses have high variance....

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2019 Mar 06

STAT 300: Wenshuo Wang

12:00pm to 1:00pm

Location: 

SC 705

Title: Metropolized Knockoff Sampling

Abstract: Model-X knockoffs is a wrapper that transforms essentially any feature importance measure into a variable selection algorithm, which discovers true effects while rigorously controlling the expected fraction of false positives. A frequently discussed challenge to apply this method is to construct knockoff variables, which are synthetic variables obeying a crucial exchangeability property with the explanatory variables under study. This paper introduces techniques for knockoff generation in great...

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2019 Feb 20

STAT 300: Assistant Professor Pierre Jacob

12:00pm to 1:00pm

Location: 

SC 705

Title: Recent developments on unbiased Monte Carlo methods

Abstract: Monte Carlo estimators, based on Markov chains or interacting particle systems, are typically biased when run with a finite number of iterations (or a finite number of particles). Although this is usually considered unavoidable, and negligible in the usual asymptotic sense, it is an important obstacle on the path towards scalable numerical integration on large-scale distributed computing systems. In a series of works that build on the seminal paper of Glynn and Rhee (2014), a...

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2015 Feb 02

BrownCSSem: Roger Peng

3:30pm to 5:00pm

Location: 

Room 245, 121 South Main Street, Providence
TBA
2015 Apr 09

BUStatPrSem: Fan Zhuo

4:00pm to 5:00pm

Location: 

MCS 148
Likelihood Ratio Based Tests for Markov Regime Switching
2015 Oct 27

ResearchStats: Xufei Wang

12:00pm to 1:00pm

Location: 

Science Center Rm. 705
Generalized R-squared for Detecting Non-independence
2013 Oct 21

BrownCSSem: Hao Wu

3:30pm to 5:00pm

Location: 

Room 245, 121 South Main Street, Providence
A Novel Statistical Method for Quantitative Comparison of Multiple ChIP-seq Datasets

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