2014년 제8회 통계세미나 개최 안내
- BK 세미나 시리즈 -
통계연구소에서는 다음과 같이 통계 세미나를 개최하오니 많은 참여 바랍니다.
일시: 2014년 6월 16일 (월) 오후 5시
장소: 고려대학교 정경관 618호
연사: 석혜원 교수 (Department of Psychology, Arizona State University)
Functional Generalized Structured Component Analysis
Generalized Structured Component Analysis (GSCA) is a component-based approach to structural equation modeling that enables to examine directional relationships among multiple sets of responses by combining path modeling with data reduction. In GSCA, a component or a weighted composite is obtained from each set of observed responses in such a way that, given hypothesized relationships among the sets, it captures the variation in both observed responses and components as much as possible. In this talk, I will introduce a recent extension of GSCA, functional GSCA, for the analysis of so-called functional data. Functional data refer to the data that are considered to arise from an underlying smooth function varying over a continuum such as time or space. Familiar examples include motion capture data, neuroimaging data, eye-tracking data, polygraph data, and physiological data. The emergence of sophisticated measurement tools, e.g., motion capture devices, handheld computers, Bluetooth devices, eye-trackers, and brain scanners, has facilitated the collection of functional data and the needs for statistical methods to analyze such new types of data are increasing. Functional GSCA is of use to examine how response trajectories are related with other variables and when such relationships exist. Technical details and empirical examples will be presented.
고려대학교 통계연구소