Stat-DS Colloquium

Modelling function-valued processes with non-separable and/or non-stationary covariance structure

Speaker:Prof. Jian Qing Shi (史建清教授)

Time:Jan 15, 2020, 15:00-16:00

Location:Room 206, Lychee Hills No.1

报告简介  Abstract

Separability of the covariance structure is a common assumption for function-valued processes defined on two- or higher-dimensional domains. This assumption is often made to obtain an interpretable model or due to difficulties in modelling a potentially complex covariance structure, especially in the case of sparse designs. We proposed to use Gaussian processes with flexible parametric covariance kernels which allow interactions between the inputs in the covariance structure. When we use suitable covariance kernels, the leading eigen-surfaces of the covariance operator can explain well the main modes of variation in the functional data, including the interactions between the inputs. The results are demonstrated by simulation studies and by applications to real world data.

嘉宾简介  About the Speaker

Jian Qing Shi,博士,英国纽卡斯尔大学(Newcastle University)统计学教授,英国国家数据科学研究院(Alan Turing Institute) 的图灵研究员 (Turing Fellow)。南京大学计算数学理学学士,东南大学统计学硕士,香港中文大学统计学博士,主要研究方向为函数型数据的统计分析。在国际学术刊物上发表高水平学术论文多篇,包括统计顶级期刊JASA, JRSS B 和 Biometrika。目前担任英国皇家统计协会《应用统计》副主编,Guest AE for JRSS discussion paper,英国 EPSRC数学学科科研经费评审委员会委员,英国APTS研究生课程全国执行委员会委员,校云计算和大数据研究培训中心副主任,院应用统计和概率研究室主任,院统计学科研究生主任,院国际事务主任,院科研委员会委员,院招生委员会委员。2008年获邀任剑桥大学世界最顶级数学学院之一牛顿学院访问研究员,2011年获美国统计协会非参数统计分会年度最佳论文奖,2012年获英国 Wellcome trust Health Innovation Challenge Fund,共计210万英镑。2011年在最著名统计学出版社Chapman & Hall 出版专著:Gaussian Process Regression Analysis for Functional Data。

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