| dc.contributor | 統計系 | en_US |
| dc.creator (作者) | 余清祥 | zh_TW |
| dc.creator (作者) | Wang, Tai-Chi ;Yue,Ching-Syang Jack | en_US |
| dc.date (日期) | 2013.04 | en_US |
| dc.date.accessioned | 3-十二月-2013 18:16:10 (UTC+8) | - |
| dc.date.available | 3-十二月-2013 18:16:10 (UTC+8) | - |
| dc.date.issued (上傳時間) | 3-十二月-2013 18:16:10 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/62093 | - |
| dc.description.abstract (摘要) | Spatial data often possess multiple components, such as local clusters and global clustering, and these effects are not easy to be separated. In this study, we propose an approach to deal with the cases where both global clustering and local clusters exist simultaneously. The proposed method is a two-stage approach, estimating the autocorrelation by an EM algorithm and detecting the clusters by a generalized least square method. It reduces the influence of global dependence on detecting local clusters and has lower false alarms. Simulations and the sudden infant disease syndrome data of North Carolina are used to illustrate the difference between the proposed method and the spatial scan statistic. | - |
| dc.format.extent | 877829 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Spatial and Spatio-temporal Epidemiology, 0,0 | en_US |
| dc.subject (關鍵詞) | Local Cluster; Spatial Global Dependence; Conditional Autocorrelated Regressive Model; Spatial Scan Statistic; EM Estimates;Generalized Least Square | en_US |
| dc.title (題名) | Spatial Clusters in a Global-Dependence Model | en_US |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.1016/j.sste.2013.03.003 | en_US |
| dc.doi.uri (DOI) | http://dx.doi.org/http://dx.doi.org/10.1016/j.sste.2013.03.003 | en_US |
