Welcome to MINDS!

Established in 2020, POSTECH Mathematical Institute for Data Science (MINDS) is the community of researchers in the areas of fundamental data science, machine learning, artificial intelligence, scientific computing, and humanitarian data science. MINDS mission is to provide a platform for collaboration among researchers and to provide various opportunities for students in data science. MINDS also aims to use our data science research to serve our local and global communities pursuing humanitarian data science.

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MINDS SEMINAR

MINDS-CM2LA Seminar Series | Joseph Kang (Research Mathematical Statistician, U.S. Census Bureau) - On machine learning models for incomplete survey data

MINDS SEMINAR
period : 2023-12-07 ~ 2023-12-07
time : 17:00:00 ~ 18:00
개최 장소 : Math Bldg 404 & Online streaming (Zoom)
Topic : On machine learning models for incomplete survey data
개요
Date 2023-12-07 ~ 2023-12-07 Time 17:00:00 ~ 18:00
Speaker Joseph Kang Affiliation Research Mathematical Statistician, U.S. Census Bureau
Place Math Bldg 404 & Online streaming (Zoom) Streaming link ID : 688 896 1076 / PW : 54321
Topic On machine learning models for incomplete survey data
Contents Machine learning (ML) methods have been developed to improve the accuracy of predictions for unobserved data. Unobserved data are prevalent in many social surveys conducted by US statistical agencies and pose a significant challenge to achieving unbiased results. From the perspective of survey nonresponse, ML methods can be effectively employed to reduce the bias of survey outcomes. Despite the advancement of ML, its popularity has yet to gain widespread acceptance among statistical researchers who are unfamiliar with the challenges of interpretability in ML methods. This talk will introduce a popular ML method used in survey science and provide guidance on how to avoid relying blindly on this method and instead calibrate it with other related statistical methods. The application of ML-based methods will be illustrated via a well-known simulation study design by Kang and Schafer (2007).
MinDS MinDS · 2023-11-23 14:48 · Views 284

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