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DTSTAMP:20260729T021811Z
DESCRIPTION:Eric B. Laber\, PhD\n\nJames B. Duke Distinguished Professor\n	D
 ept of Statistical Sciences and Biostatistics and Bioinformatics\n	Duke Uni
 versity\n\nWHEN: Wednesday\, March 27\, 2024\, from 3:30 to 4:30 p.m.\n	WHE
 RE: Hybrid | 2001 McGill College Avenue\, Room 1201\; Zoom\n	NOTE: Eric Lab
 er will be presenting in-person\n\nAbstract\n\nRespondent-driven sampling 
 (RDS) is a network-based sampling strategy used to study hidden population
 s for which no sampling frame is available. In each epoch of an RDS study\
 , the current wave of study participants are incentivized to recruit the n
 ext wave through their social connections. The success and efficiency of R
 DS can depend critically on attributes of incentives and the underlying (l
 atent) network structure. We propose a reinforcement learning-based adapti
 ve RDS design to optimize some measure of study utility\, e.g.\, efficienc
 y\, treatment dissemination\, reach\, etc. Our design is based on a branch
 ing process approximation to the RDS process\, however\, our proposed post
 -study inferential procedures apply to general network models even when th
 e network is not fully identified. Simulation experiments show that the pr
 oposed design provides substantial gains in efficiency over static and two
 -step RDS procedures.\n\nSpeaker bio\n\nPlease visit: https://laber-labs.c
 om\n\n \n
DTSTART:20240327T193000Z
DTEND:20240327T203000Z
SUMMARY:Reinforcement Learning for Respondent-Driven Sampling
URL:https://www.mcgill.ca/epi-biostat-occh/channels/event/reinforcement-lea
 rning-respondent-driven-sampling-356192
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