报告人：Lily Wang (George Mason University)

时间：2021年10月28日星期四9:00-10:00

腾讯会议ID：593 985 941

摘要：

Nowadays, we are living in the era of “Big Data.” A significant portion of big data is big spatial data captured through advanced technologies or large-scale simulations. Explosive growth in spatial and spatiotemporal data emphasizes the need for developing new and computationally efficient methods and credible theoretical support tailored for analyzing such large-scale data. Parallel statistical computing has proved to be a handy tool when dealing with big data. In general, it uses multiple processing elements simultaneously to solve a problem. However, it is hard to execute the conventional spatial regressions in parallel. This talk will introduce a novel parallel smoothing technique for generalized partially linear spatially varying coefficient models, which can be used under different hardware parallelism levels. Moreover, conflated with concurrent computing, the proposed method can be easily extended to the distributed system. Regarding the theoretical support of estimators from the proposed parallel algorithm, we first establish the asymptotical normality of linear estimators. Secondly, we show that the spline estimators reach the same convergence rate as the global spline estimators. The proposed method is evaluated through extensive simulation studies and an analysis of the US loan application data.

报告人简介：

Lily Wang，美国George Mason University统计学教授，国际数理统计学会当选会士(IMS Fellow)，美国统计协会当选会士(ASA Fellow)，国际统计学会当选会员(ISI Member)；目前担任Journal of the Royal Statistical Society, Series B, Journal of Nonparametric Statistics, Statistical Analysis and Data Mining等统计学重要期刊的编委。曾在美国爱荷华州立大学（2014-2021）和佐治亚大学（2007-2014）任教。于2007 年在密歇根州立大学获得博士学位。主要研究方向包括非/半参数回归、具有复杂特征的数据对象的统计分析、函数型数据分析、时空数据分析及抽样调查等。研究内容涉及统计学、数学和计算机科学领域，以及数据科学和大数据分析等相关问题。提出的方法在工程学、神经影像学、流行病学、环境研究、经济学和生物医学中都有着广泛的应用。

邀请人：张园园