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英国伦敦经济与政治科学学院

姚琦伟教授 英国伦敦经济与政治科学学院  (London School of Economics and Political Sciences)统计系教授，2006 -2009 期间任系主任，英国皇家统计学会会士，美国统计协会会士，数理统计学会会士，国际统计研究学会选举会员。

姚琦伟教授是国际知名的统计学家，一直从事统计学的教学和科研工作，主要研究领域为：时间序列分析、时空过程分析、金融计量经济学。他在非线性和高维时间序列方面的研究国际领先。姚琦伟教授迄今已发表学术论文80多篇， 并获得EPSRC, BBSRC等英国国家基金会支持的多项研究基金项目。其专著《非线性时间序列：非参数及参数方法》（与范剑青合著）于2003年由Springer 出版，《计量金融简要》（与范剑青合著）于2017年由剑桥出版社出版。姚琦伟教授已担任包括Annals of Statistics，Journal of the American Statistics Association, Journal of the Royal Statistical Society (Series B) 等多个顶级杂志副主编，并曾任 Statistica Sinica的联合主编。姚琦伟教授还曾为巴克莱银行，法国电力公司以及Winton资本等多家企业提供咨询。

While it is common practice in applied network analysis to report various standard network summary statistics, these numbers are rarely accompanied by some quantification of uncertainty.  Yet any error inherent in the measurements underlying the construction of the network, or in the network construction procedure itself, necessarily must propagate to any summary statistics reported.  Here we study the problem of estimating the density of an arbitrary subgraph, given a noisy version of some underlying network as data.  Under a simple model of network error, we show that consistent estimation of such densities is impossible when the rates of error are unknown and only a single network is observed.  Next, focusing first on the problem of estimating the density of edges from noisy networks, as a canonical prototype of the more general problem, we develop method-of-moment estimators of network edge density and error rates for the case where a minimal number of network replicates are available.  These estimators are shown to be asymptotically normal as the number of vertices increases to infinity.  We also provide confidence intervals for quantifying the uncertainty in these estimates based on either the asymptotic normality or a bootstrap scheme. We then present a generalization of these results to higher-order subgraph densities, and illustrate with the case of two-star and triangle densities. Bootstrap confidence intervals for those high-order densities are constructed based on a new algorithm for generating a graph with pre-determined counts for edges, two-stars, and triangles. The algorithm is based on the idea of edge-rewiring, and is of some independent interest. We illustrate the use of the proposed methods in the context of gene coexpression networks.