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】【打印】【关闭窗口 来源:bob体育APP本站原创 作者:统计与数学学院 编辑:张薇 发布时间:2020-01-06
      报告题目:Modelling function-valued processes with non-separable and/or non-stationary covariance structure
      时间:2020年1月7日(周二)10:00 a.m.

      摘要: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.

      史建清教授,现为英国纽卡斯尔大学(Newcastle University)数学、统计与物理学院教授,从2018年10月起担任英国艾伦.图灵研究院(The Alan Turing Institute, 即英国国家数据科学和人工智能研究院)研究员,目前是英国皇家统计协会会士(Fellow of the Royal Statistical Society)、泛华统计协会(International Chinese Statistical Association)会员、英国皇家统计协会期刊评论文章的客座编委(Guest Associate Editor of Journal of the Royal Statistical Society ),统计学杂志Journal of the Royal Statistical Society(Series C,应用统计)、Statistical and Probability letters、British Journal of Mathematics & Computer Sciences等的编委(Associate Editor)。
    史建清教授于1996年在香港中文大学获得统计学博士学位,并先后到英国华威大学(The University of Warwick)和格拉斯哥大学(The University of Glasgow)从事博士后研究工作,于2002年入职纽卡斯尔大学,分别于2008年和2015年受邀到剑桥大学艾萨克·牛顿数学科学研究院(Isaac Newton Institute for Mathematical Sciences, Cambridge University)和美国统计与应用数学科学研究院(Statistical and Applied Mathematical Sciences Institute)进行学术访问。
    史建清教授的主要研究领域包括函数型数据分析、缺失数据及模型不确定性分析、协方差结构分析和潜变量模型等,已在国际学术期刊上发表多篇高水平论文,包括统计学顶级期刊Journal of the American Statistical Association、Journal of the Royal Statistical Society(Series B)和Biometrika,目前已发表学术论文70余篇,在国际权威出版社Chapman & Hall出版专著1部,即《函数型数据的高斯过程回归分析》(Gaussian Process Regression Analysis for Functional Data)。此外,史建清教授2011年在期刊Journal of Nonparametric Statistics上发表的论文Bayesian single-index model using a Gaussian process prior获得美国统计协会颁发的非参数统计主题最佳论文奖,2014年的论文Automatic Assessment of Upper Limb Function During Play of the Action Video Game, Circus Challenge: Validity and Sensitivity to Change获得美国电气与电子工程师协会(IEEE)颁发的最佳论文奖。史建清教授也曾多次获得英国自然科学基金(EPSRC,Engineering And Physical Sciences Research Council)、英国国家医学基金(MRC,Medical Research Council))和英国卫生改革挑战基金(Welcome Trust Health Innovation Challenge Fund)等的资助。

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