学术报告
报告题目：Spline Smoothing with Large Data
报告人：王跃东 University of California, Santa Barbara 教授
报告时间：2019年3月8日（周五） 上午 10: 00
报告地点：博识楼434室
报告摘要：Spline smoothing is a widely used nonparametric method that allows data to speak for themselves. Due to its complexity and flexibility, fitting smoothing spline models is usually computationally intensive which may become prohibitive with large datasets. To overcome memory and CPU limitations, we propose four divide and recombine (D&R) approaches for fitting cubic splines with large datasets. We consider two approaches to divide the data: random and sequential. For each approach of division, we consider two approaches to recombine. These D&R approaches are implemented in parallel without
communication. Extensive simulations show that these D&R approaches are scalable and have comparable performance as the method that uses the whole data. The sequential D&R approaches are spatially adaptive which lead to better performance than the method
that uses the whole data when the underlying function is spatially inhomogeneous.
报告人简介：王跃东博士，美国加州大学圣巴巴拉分校终身教授，是统计学界具有卓越贡献的研究者，为国际统计学院当选会士、美国统计学会当选会士、英国皇家学会会士，是国际数理统计协会、泛华统计协会、国际统计科学学会的会员。致力于统计学方法及其应用的研究，围绕光滑样条、混合效应模型、生存分析、纵向数据、微阵列数据分析等方向，在统计学国际顶尖学术期刊发表高水平论文百十余篇。
欢迎感兴趣的老师和研究生参加
报告题目：Spline Smoothing with Large Data
报告人：王跃东 University of California, Santa Barbara 教授
报告时间：2019年3月8日（周五） 上午 10: 00
报告地点：博识楼434室
报告摘要：Spline smoothing is a widely used nonparametric method that allows data to speak for themselves. Due to its complexity and flexibility, fitting smoothing spline models is usually computationally intensive which may become prohibitive with large datasets. To overcome memory and CPU limitations, we propose four divide and recombine (D&R) approaches for fitting cubic splines with large datasets. We consider two approaches to divide the data: random and sequential. For each approach of division, we consider two approaches to recombine. These D&R approaches are implemented in parallel without
communication. Extensive simulations show that these D&R approaches are scalable and have comparable performance as the method that uses the whole data. The sequential D&R approaches are spatially adaptive which lead to better performance than the method
that uses the whole data when the underlying function is spatially inhomogeneous.
报告人简介：王跃东博士，美国加州大学圣巴巴拉分校终身教授，是统计学界具有卓越贡献的研究者，为国际统计学院当选会士、美国统计学会当选会士、英国皇家学会会士，是国际数理统计协会、泛华统计协会、国际统计科学学会的会员。致力于统计学方法及其应用的研究，围绕光滑样条、混合效应模型、生存分析、纵向数据、微阵列数据分析等方向，在统计学国际顶尖学术期刊发表高水平论文百十余篇。
欢迎感兴趣的老师和研究生参加