DS+, BK 2023년도 제 16회 통계세미나 개최 안내(11/17(금))
DS플러스
2023-11-15
2023년도 제 16회 BK 통계 세미나 개최를 안내드립니다.
고려대학교 통계학과 통계연구소, BK21 통계학교육연구팀과 DS+ 사업단 주최로 이루어지는 세미나입니다.
일시 : 2023년 11월 17일 (금) 오전 11시
장소 : 고려대학교 정경관 206호
연사 : 노호석 교수 (숙명여대 통계학과)
주제 :
Advancing Statistical Approaches with Al: Lessons from My Exploration in the Development of Measurement Error Models
Abstract :
Measurement errors are a common occurrence in our daily lives, and extensive efforts have been devoted to addressing scenarios where a single covariate is measured with error. However, practical situations often involve the measurement of more than one variable with correlated errors. In the initial segment of my presentation, I will introduce a new approach to estimating a bivariate multiplicative measurement error model when both covariates are subject to multiplicative measurement errors. Our method posits that the multiplicative errors should adhere to a bivariate lognormal distribution. We propose an estimation technique by approximating the joint density of the scaled error-free variables using piecewise constant functions on the unit square. Transitioning to the second part of the talk. I will explore how the same issue can be addressed from an AI perspective. In doing so, I will delve into the role of Al in expanding the repertoire of statistical methodologies available for data analysis.
홍보 자료 : 첨부파일 확인 부탁드립니다
[온라인 세미나 참여 링크]
ZOOM 링크
https://korea-ac-kr.zoom.us/j/81311119137?pwd=R0Z3SDM1ZG5kdzRzQnRkQ0cvSXNkdz09
- Zoom ID: 813 1111 9137
- Password: Kustat123@
앞으로는 해당 링크를 2학기동안 계속해서 사용할 예정입니다.
고려대학교 통계학과 통계연구소, BK21 통계학교육연구팀과 DS+ 사업단 주최로 이루어지는 세미나입니다.
일시 : 2023년 11월 17일 (금) 오전 11시
장소 : 고려대학교 정경관 206호
연사 : 노호석 교수 (숙명여대 통계학과)
주제 :
Advancing Statistical Approaches with Al: Lessons from My Exploration in the Development of Measurement Error Models
Abstract :
Measurement errors are a common occurrence in our daily lives, and extensive efforts have been devoted to addressing scenarios where a single covariate is measured with error. However, practical situations often involve the measurement of more than one variable with correlated errors. In the initial segment of my presentation, I will introduce a new approach to estimating a bivariate multiplicative measurement error model when both covariates are subject to multiplicative measurement errors. Our method posits that the multiplicative errors should adhere to a bivariate lognormal distribution. We propose an estimation technique by approximating the joint density of the scaled error-free variables using piecewise constant functions on the unit square. Transitioning to the second part of the talk. I will explore how the same issue can be addressed from an AI perspective. In doing so, I will delve into the role of Al in expanding the repertoire of statistical methodologies available for data analysis.
홍보 자료 : 첨부파일 확인 부탁드립니다
[온라인 세미나 참여 링크]
ZOOM 링크
https://korea-ac-kr.zoom.us/j/81311119137?pwd=R0Z3SDM1ZG5kdzRzQnRkQ0cvSXNkdz09
- Zoom ID: 813 1111 9137
- Password: Kustat123@
앞으로는 해당 링크를 2학기동안 계속해서 사용할 예정입니다.