Scalable Spatiotemporally Varying Coefficient Modelling with Bayesian Kernelized Tensor Regression

arXiv:2109.00046v4 Announce Type: replace-cross
Abstract: As a regression technique in spatial statistics, the spatiotemporally varying coefficient model (STVC) is an important tool for discovering nonstationary and interpretable response-covariate associations over both space and time. However, it is difficult to apply STVC for large-scale spatiotemporal analyses due to its high computational cost. To address this challenge, we summarize the spatiotemporally varying coefficients using a third-order tensor structure and propose to reformulate the spatiotemporally varying coefficient model as a special low-rank tensor …

abstract apply arxiv bayesian computational cs.lg however modelling regression scalable scale space space and time spatial statistics stat.ml tensor tool type