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报告题目: Spatial Factor Modeling: A Bayesian Matrix-Normal Approach for Massive Spatial Data with Missing Observations
报 告 人: 张璐
报告人所在单位: 哥伦比亚大学统计系
报告日期: 2021-09-22 星期三
报告时间: 10:00-11:00
报告地点: 腾讯会议号: 672 389 820,会议密码: 200433
   
报告摘要:

The last decade has witnessed substantial developments in scalable models for univariate spatial processes, but such methods for multivariate spatial processes, especially when the number of outcomes is moderately large, are limited in comparison. In this work, we extend scalable modeling strategies for a single process to multivariate processes. We pursue Bayesian inference which is attractive for full uncertainty quantification of the latent spatial process. Our approach exploits distribution theory for the Matrix-Normal distribution, which we use to construct scalable versions of a hierarchical linear model of coregionalization (LMC) and spatial factor models that deliver inference over a high-dimensional parameter space including the latent spatial process.

9.22.pdf

   
本年度学院报告总序号: 224

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