[ugrads] FW: [grads] Applied Mathematics Research Showcase: Statistics
ycao33 at hawk.iit.edu
Thu Mar 8 08:54:37 CST 2018
Just remind, we have a talk today noon.
Yue Cao, PHD candidate
IIT SIAM Student Chapter President
Department of Applied Mathematics
Illinois of Institute of Technology
Office: John T. Rettaliata Engineering Center, Room 232
Email: ycao33 at hawk.iit.edu
From: Kan Zhang
Sent: Wednesday, March 7, 2018 5:20 PM
To: ugrads at math.iit.edu; grads at math.iit.edu; hawklink__iit_c96910eb-63f6-40bd-82e1-b05c89965674 at relay.collegiatelink.net
Subject: [grads] Applied Mathematics Research Showcase: Statistics
Please join us Thursday, March 08, for free lunch and a chance to see what's new in the Applied Mathematics department!
This is part of a series of special seminars that IIT's SIAM student chapter and Applied Mathematics department host jointly. Each seminar features faculty members briefly discussing their recent and ongoing research over lunch. If you are a new graduate student or an advanced undergraduate looking for research topics or advisors, or if you just want to know what your colleagues are working on, then this event is for you.
This seminar will feature research in Statistics, with a talk by Dr. Lulu Kang.The talk will take place on Thursday, March 08, 12:45-1:45 in room RE 106. Lunch will be provided. It is open to all, but Applied Mathematics students are strongly encouraged to attend. We hope to see you there!
Title: Discrepancy-Based Design for A/B Testing Experiments
The aim of this talk is to introduce a new design of experiment method for A/B tests. A/B tests (or "A/B/n tests'') refer to the experiments conducted to estimate the treatment effect(s) of a two-level or multi-level controllable experimental factor. To conclude whether the treatment effect is significant, the common practice is to use a completely randomized design and perform the hypothesis test on the sample difference-in-mean estimate. However, such estimator is not always accurate when the covariates of the test units affect the responses, especially for the small to medium-sized experiments. To overcome this issue, we propose the discrepancy-based design which significantly improves the accuracy of the estimates of the treatment effects, as shown both theoretically and through simulations. More importantly, the design approach is model-free, and thus it makes the estimation robust to the model assumption. Also, it can be applied to both continuous and discrete/categorical types of responses. We develop two optimization procedures to minimize the discrepancy criterion for both offline and online experiments.
IIT SIAM Student Chapter Secretary
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