A1099
Title: On partial envelope approach for modeling spatial-temporally dependent data
Authors: Wenbo Wu - University of Texas at San Antonio (United States) [presenting]
Abstract: In the new era of big data, modeling multivariate spatial-temporally dependent data is a challenging task due to not only the high dimensionality of the features but also complex spatial-temporal associations among the observations across different locations and time points. To improve the estimation efficiency, a spatial-temporal partial envelope model is proposed, which is parsimonious and effective in modeling high-dimensional spatial-temporal data. The partial envelope model is proposed under a linear coregionalization model framework, which allows for a heterogeneous spatial-temporal covariance structure for different components of the response vector. The maximum likelihood estimator for the proposed model can be obtained through a Grassmann manifold optimization. An asymptotic result is obtained for the estimator, and thorough simulation studies are conducted to demonstrate the soundness and effectiveness of the proposed method. The proposed model is also applied to analyze the crowdsourcing weather data collected from personal weather stations in the city of San Antonio, TX, of the United States.
