A1001
Title: A partial envelope approach for modeling multivariate spatial-temporal data
Authors: Reisa Widjaja - University of Wisconsin - La Crosse (United States) [presenting]
Abstract: In the new era of big data, modeling multivariate spatial-temporally dependent data is a challenging task due to the dimensionality of the features and 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 was proposed under a linear coregionalization model framework, which allows 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. A complete asymptotic result is obtained for the estimator, and thorough empirical simulations 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 Syracuse, NY, in the United States.
