A1225
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-temporal data is a challenging task due to both the high dimensionality of the features and complex associations among the responses 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 heterogeneous covariance structures for different variables of the response vector. The asymptotic behavior of the estimator is studied and a thorough simulation study is 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 Syracuse, New York, United States.
