题目：Copula-based approaches for analyzing non-Gaussian spatial data

报告人：Huixia Judy Wang

讲座时间：2022年11月9日，上午9：00-10：00

地点：线下综合楼644，线上腾讯会议626-250-828

摘要：Many existing methods for analyzing spatial data rely on the Gaussian assumption, which is violated in many applications such as wind speed, precipitation and COVID mortality data. In this talk, I will discuss several recent developments of copula-based approaches for analyzing non-Gaussian spatial data. First, I will introduce a copula-based spatio-temporal model for analyzing spatio-temporal data and a semiparametric estimator. Second, I will present a copula-based multiple indicator kriging model for the analysis of non-Gaussian spatial data by thresholding the spatial observations at a given set of quantile values. The proposed algorithms are computationally simple, since they model the marginal distribution and the spatio-temporal dependence separately. Instead of assuming a parametric distribution, the approaches model the marginal distributions nonparametrically and thus offer more flexibility. The methods will also provide convenient ways to construct both point and interval predictions based on the estimated conditional quantiles. I will present some numerical results including the analyses of a wind speed and a precipitation data. If time allows, I will also discuss a recent work on copula-based approach for analyzing count spatial data.

报告人简介：Huixia Judy Wang教授现任美国乔治华盛顿大学统计系主任。她2006年毕业于美国伊利诺伊大学香槟分校统计系。是美国统计学会、国际数理统计学会会员，主要研究方向为生物统计学、极值理论及应用、高维统计推断、纵向数据分析、缺失数据、分位数回归、亚组分析、生存分析等。在统计学的顶级期刊JASA、Biometrics、JRSS-B等刊物发表论文70余篇。