Copula-based approaches for analyzing non-Gaussian spatial data

报告人：Huixia(Judy) Wang

报告地点：腾讯会议ID:929-616-649

报告时间：2022年12月7日星期三10:30-12:00

报告摘要:

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.

主讲人简介：

Huixia(Judy) Wang，教授，现任美国乔治华盛顿大学统计系主任。于1999、 2002年分别获复旦大学本科和硕士学位，2006年毕业于美国伊利诺伊大学香槟分校统计系获博士学位。 曾于2012年获得美国科学研究基金Career Award及国际数理协会研究基金Tweedie Award。 研究兴趣主要包括：生物信息学、生物统计学、缺失数据分析、分位数回归和变量选择等。 担任JASA及AOS等国际顶尖统计期刊副主编，并主持了多项美国自然科学基金项目，已在国际顶尖学术期刊发表论文70多篇。