A0825
Title: Dimension reduction for spatially correlated data
Authors: Hossein Moradi Rekabdararkolaee - South Dakota State University (United States) [presenting]
Abstract: Dimension reduction provides a useful tool for statistical data analysis with high-dimensional data. A parsimonious multivariate spatial regression model is developed with a non-separable covariance function. The efficacy of this new solution is illustrated through simulation studies and real data analysis. It is shown that for cases where the marginal spatial correlations are different from each other, the proposed non-separable model provides better estimation and inference than the related separable model and provides tighter inference than a non-separable spatial model without dimension reduction when there is immaterial variation in the data.
