Space-time stick-breaking processes for small area disease cluster estimationopen access
- Authors
- Hossain, Md Monir; Lawson, Andrew B.; Cai, Bo; Choi, Jungsoon; Liu, Jihong; Kirby, Russell S.
- Issue Date
- Mar-2013
- Publisher
- Kluwer Academic Publishers
- Keywords
- Cluster; Dependence; Dirichlet process mixture; Space-time; Stick-breaking processes
- Citation
- Environmental and Ecological Statistics, v.20, no.1, pp.91 - 107
- Indexed
- SCIE
SCOPUS
- Journal Title
- Environmental and Ecological Statistics
- Volume
- 20
- Number
- 1
- Start Page
- 91
- End Page
- 107
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/163161
- DOI
- 10.1007/s10651-012-0209-0
- ISSN
- 1352-8505
- Abstract
- We propose a space-time stick-breaking process for the disease cluster estimation. The dependencies for spatial and temporal effects are introduced by using space-time covariate dependent kernel stick-breaking processes. We compared this model with the space-time standard random effect model by checking each model's ability in terms of cluster detection of various shapes and sizes. This comparison was made for simulated data where the true risks were known. For the simulated data, we have observed that space-time stick-breaking process performs better in detecting medium- and high-risk clusters. For the real data, county specific low birth weight incidences for the state of South Carolina for the years 1997-2007, we have illustrated how the proposed model can be used to find grouping of counties of higher incidence rate.
- Files in This Item
-
- Appears in
Collections - 서울 자연과학대학 > 서울 수학과 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/163161)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.