|제목||[학술세미나] [학과세미나] 5월 24일(목) 11시 특별세미나 안내|
|내용||[학과세미나] 5월 24일(목) 11시 특별세미나 안내
▪ 제목 : Modeling and Integrating Different Sources of Spatio-temporal Data
▪ 연사 : Jun Zhu (University of Wisconsin-Madison)
▪ 일시 : 2018년 5월 24일(목) AM 11:00 – 12:00
▪ 장소 : 25동 405호
Rapid technological advances have substantially improved the data collection capacity in occupational exposure assessment. However, advanced statistical methods for analyzing such data and drawing proper inference remain limited. In this talk, we consider spatio-temporal methodology that combines data from both roving and static sensors for data processing and hazard mapping across space and over time in an indoor environment. We compare the new method with the current industry practice, demonstrating the advantages of the new method in occupational health. In particular, a class of semiparametric temporal geostatistical models is considered with non-separable and non-stationary spatio-temporal covariance functions. A profile likelihood based model fitting procedure is developed that allows fusion of the two types of data. To account for potential differences between the static and roving sensors, we extend the model to have non-homogenous measurement error variances. Our methodology is applied to a case study conducted in an engine test facility and dynamic hazard maps are drawn to show features in the data that would have been missed by existing approaches, but are captured by the new method.
세미나 안내_180524_Jun Zhu.hwp [14.5KB]