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Most existing approaches focus on predicting multivariate Gaussian spatial processes, but as the data may consist of non-Gaussian (or mixed type) variables, this creates two challenges: (1) how to accurately capture the dependencies among different data types, both Gaussian and non-Gaussian; and (2) how to efficiently predict multivariate non-Gaussian spatial processes. In this article, we propose a generic approach for predicting multiple response variables of mixed types. The proposed approach accurately captures cross-spatial dependencies among response variables and reduces the computational burden by projecting the spatial process to a lower dimensional space with knot-based techniques. Efficient approximations are provided to estimate posterior marginals of latent variables for the predictive process, and extensive experimental evaluations based on both simulation and real-life datasets are provided to demonstrate the effectiveness and efficiency of this new approach.<\/jats:p>","DOI":"10.1145\/3022669","type":"journal-article","created":{"date-parts":[[2017,3,27]],"date-time":"2017-03-27T12:25:10Z","timestamp":1490617510000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Spatial Prediction for Multivariate Non-Gaussian Data"],"prefix":"10.1145","volume":"11","author":[{"given":"Xutong","family":"Liu","sequence":"first","affiliation":[{"name":"ebay Inc"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Chen","sequence":"additional","affiliation":[{"name":"University at Albany, SUNY, Albany, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yen-Cheng","family":"Lu","sequence":"additional","affiliation":[{"name":"Virginia Tech"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang-Tien","family":"Lu","sequence":"additional","affiliation":[{"name":"Virginia Tech"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,3,27]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007589505425"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.64"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2008.00663.x"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.85"},{"volume-title":"Multi-task Gaussian process prediction. 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