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홈 > 학술정보 >학술행사 > 세미나
2017 KAIST Math. Colloquium

Title
Hierarchical Spatially Varying Coefficient Process Model
Speaker
김희영   (KAIST )
Date
2017-10-12 16:15:00
Host
KAIST
Place
Room 1501, Building E6-1
Abstract
The spatially varying coefficient process model is a nonstationary approach to explaining spatial heterogeneity by allowing coefficients to vary across space. To accommodate geographically hierarchical data, we develop a methodology for generalizing this model. We consider two-level hierarchical structures and allow for the coefficients of both low-level and high-level units to vary over space. We assume that the spatially varying low-level coefficients follow the multivariate Gaussian process, and the spatially varying high-level coefficients follow the multivariate simultaneous autoregressive model that we develop by extending the standard simultaneous autoregressive model to incorporate multivariate data. We apply the proposed model to transaction data of houses sold in 2014 in a part of the city of Los Angeles. The results show that the proposed model predicts housing prices and fits the data effectively.
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