报告题目：Modelling function-valued processes with non-separable and/or non-stationary covariance structure
报告人：史建清教授（英国纽卡斯尔大学）
报告时间：2019年6月20日(星期四)14:00-15:00
报告地点：尚贤楼706
主持人：曹春正教授
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报告内容简介：
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 using 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.
报告人简介：
史建清，英国纽卡斯尔大学数学与统计学院教授、大数据与云计算博士生研究中心副主任，英国图灵研究所研究员，我校非全时教授，统计学国际知名学者，主要研究方向包括函数型数据分析、缺失数据和模型不确定性分析、医学统计以及大规模系统监控等，历任Journal of the Royal Statistical Society (Series C)等多家统计学顶尖杂志编委。在国际权威出版社Chapman & Hall出版专著《Gaussian Process Regression Analysis for Functional Data》，关于函数型数据分析等系列研究成果发表在Journal of the American Statistical Association、Journal of the Royal Statistical Society Series B、Biometrika、Biometrics等统计学顶尖杂志。合作论文荣获非参数统计杂志和美国统计协会非参数统计主题最佳论文奖。
数学与统计学院
江苏省统计科学研究基地
2019年6月18日