Detecting Spatial Clusters via a Mixture of Dirichlet Processes
We proposed an approach that has the ability to detect spatial clusters with skewed or irregular distributions. A mixture of Dirichlet processes (DP) was used to describe spatial distribution patterns. The effects of different batches of data collection efforts were also modeled with a Dirichlet pro...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2018/3506794 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832559718198935552 |
|---|---|
| author | Meredith A. Ray Jian Kang Hongmei Zhang |
| author_facet | Meredith A. Ray Jian Kang Hongmei Zhang |
| author_sort | Meredith A. Ray |
| collection | DOAJ |
| description | We proposed an approach that has the ability to detect spatial clusters with skewed or irregular distributions. A mixture of Dirichlet processes (DP) was used to describe spatial distribution patterns. The effects of different batches of data collection efforts were also modeled with a Dirichlet process. To cluster spatial foci, a birth-death process was applied due to its advantage of easier jumping between different numbers of clusters. Inferences of parameters including clustering were drawn under a Bayesian framework. Simulations were used to demonstrate and assess the method. We applied the method to an fMRI meta-analysis dataset to identify clusters of foci corresponding to different emotions. |
| format | Article |
| id | doaj-art-b48a9d57666d4eea825cc34937840bb9 |
| institution | Kabale University |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-b48a9d57666d4eea825cc34937840bb92025-02-03T01:29:23ZengWileyJournal of Probability and Statistics1687-952X1687-95382018-01-01201810.1155/2018/35067943506794Detecting Spatial Clusters via a Mixture of Dirichlet ProcessesMeredith A. Ray0Jian Kang1Hongmei Zhang2Division of Epidemiology, Biostatistics, and Environmental Health, University of Memphis, 220 Robison Hall, Memphis, TN 38152, USADepartment of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USADivision of Epidemiology, Biostatistics, and Environmental Health, University of Memphis, 224 Robison Hall, Memphis, TN 38152, USAWe proposed an approach that has the ability to detect spatial clusters with skewed or irregular distributions. A mixture of Dirichlet processes (DP) was used to describe spatial distribution patterns. The effects of different batches of data collection efforts were also modeled with a Dirichlet process. To cluster spatial foci, a birth-death process was applied due to its advantage of easier jumping between different numbers of clusters. Inferences of parameters including clustering were drawn under a Bayesian framework. Simulations were used to demonstrate and assess the method. We applied the method to an fMRI meta-analysis dataset to identify clusters of foci corresponding to different emotions.http://dx.doi.org/10.1155/2018/3506794 |
| spellingShingle | Meredith A. Ray Jian Kang Hongmei Zhang Detecting Spatial Clusters via a Mixture of Dirichlet Processes Journal of Probability and Statistics |
| title | Detecting Spatial Clusters via a Mixture of Dirichlet Processes |
| title_full | Detecting Spatial Clusters via a Mixture of Dirichlet Processes |
| title_fullStr | Detecting Spatial Clusters via a Mixture of Dirichlet Processes |
| title_full_unstemmed | Detecting Spatial Clusters via a Mixture of Dirichlet Processes |
| title_short | Detecting Spatial Clusters via a Mixture of Dirichlet Processes |
| title_sort | detecting spatial clusters via a mixture of dirichlet processes |
| url | http://dx.doi.org/10.1155/2018/3506794 |
| work_keys_str_mv | AT mereditharay detectingspatialclustersviaamixtureofdirichletprocesses AT jiankang detectingspatialclustersviaamixtureofdirichletprocesses AT hongmeizhang detectingspatialclustersviaamixtureofdirichletprocesses |