Copula-based approaches for analyzing non-Gaussian spatial data

Huixia Judy Wang (George Washington University)

Friday 16th June, 2023 15:00-16:00 Maths 311B/Zoom

Abstract

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 semiparametric approaches for analyzing non-Gaussian spatial data through copula modeling. I will first 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 analysis of precipitation data. I will briefly introduce copula-based semiparametric approaches for analyzing spatio-temporal and count spatial data.

Email wei.zhang.2@glasgow.ac.uk for Zoom link.

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