DS+, BK 2024년도 제 7회 통계세미나 개최 안내(8/28(수))
DS플러스
2024-08-22
2024년도 제 7회 BK 통계 세미나 개최를 안내드립니다.
고려대학교 통계학과 통계연구소, BK21 통계학교육연구팀과 DS+ 사업단 주최로 이루어지는 세미나입니다.
일시 : 2024년 08월 28일 (수) 오후 4시
장소 : SK미래관 520호 및 온라인(ZOOM)
연사 : Professor Wei-Yin Loh (University of Wisconsin)
주제 :
A Regression Tree Approach to Analysis of Incomplete Data
Abstract :
Analyzing data with missing values is arguably the hardest problem in statistics.
Statistical methods are often designed for completely observed data and are inapplicable if some values are missing. Although there are many techniques for imputation of missing values, the statistical properties of the resulting fitted models are unknown, except in special situations that require unverifiable and likely unjustifiable assumptions, such as "missing at random" (MAR) and "no unobserved confounding".
Using a large dataset of electronic health records of Covid-19 patients and a national consumer expenditure survey, this talk aims to show that (1) missing data should not be routinely imputed, as missingness itself can contain useful information that imputation destroys, (2) non-trivial imputation is impractical when the amount of missing data is overwhelming, and (3) the GUIDE classification and regression tree easily overcomes these difficulties. GUIDE is unique among tree algorithms in many respects, including its ability to totally avoid imputation of missing data and to explicitly display the effect of missing values in its decision tree diagrams. Literature on GUIDE and its accompanying software may be obtained at https://pages.stat.wisc.edu/~loh/guide.html.
홍보 자료 : 첨부파일 확인 부탁드립니다
Zoom 링크는 아래와 같습니다.
참가 Zoom 회의
https://korea-ac-kr.zoom.us/j/4688129273?pwd=J9RRIwd37wlI2ibPrTuHtgAgP8ugUx.1&omn=85247148333
회의 ID: 468 812 9273
암호: Kustat123@
많은 관심 부탁드립니다.
감사합니다.
고려대학교 통계학과 통계연구소, BK21 통계학교육연구팀과 DS+ 사업단 주최로 이루어지는 세미나입니다.
일시 : 2024년 08월 28일 (수) 오후 4시
장소 : SK미래관 520호 및 온라인(ZOOM)
연사 : Professor Wei-Yin Loh (University of Wisconsin)
주제 :
A Regression Tree Approach to Analysis of Incomplete Data
Abstract :
Analyzing data with missing values is arguably the hardest problem in statistics.
Statistical methods are often designed for completely observed data and are inapplicable if some values are missing. Although there are many techniques for imputation of missing values, the statistical properties of the resulting fitted models are unknown, except in special situations that require unverifiable and likely unjustifiable assumptions, such as "missing at random" (MAR) and "no unobserved confounding".
Using a large dataset of electronic health records of Covid-19 patients and a national consumer expenditure survey, this talk aims to show that (1) missing data should not be routinely imputed, as missingness itself can contain useful information that imputation destroys, (2) non-trivial imputation is impractical when the amount of missing data is overwhelming, and (3) the GUIDE classification and regression tree easily overcomes these difficulties. GUIDE is unique among tree algorithms in many respects, including its ability to totally avoid imputation of missing data and to explicitly display the effect of missing values in its decision tree diagrams. Literature on GUIDE and its accompanying software may be obtained at https://pages.stat.wisc.edu/~loh/guide.html.
홍보 자료 : 첨부파일 확인 부탁드립니다
Zoom 링크는 아래와 같습니다.
참가 Zoom 회의
https://korea-ac-kr.zoom.us/j/4688129273?pwd=J9RRIwd37wlI2ibPrTuHtgAgP8ugUx.1&omn=85247148333
회의 ID: 468 812 9273
암호: Kustat123@
많은 관심 부탁드립니다.
감사합니다.
로그인한 회원만 댓글 등록이 가능합니다.
