สืบค้นงานวิจัย
A bayesian quantile modeling for spatiotemporal relative risk: An application to adverse risk detection of respiratory diseases in South Carolina, USA
Rotejanaprasert C. - ไม่ระบุหน่วยงาน
ชื่อเรื่อง (EN): A bayesian quantile modeling for spatiotemporal relative risk: An application to adverse risk detection of respiratory diseases in South Carolina, USA
ผู้แต่ง / หัวหน้าโครงการ (EN): Rotejanaprasert C.
บทคัดย่อ (EN): Quantile modeling has been seen as an alternative and useful complement to ordinary regression mainly focusing on the mean. To directly apply quantile modeling to areal data the discrete conditional quantile function of the data can be an issue. Although jittering by adding a small number from a uniform distribution to impose pseudo-continuity has been proposed, the approach can have a great influence on responses with small values. Thus we proposed an alternative to model the quantiles of relative risk for spatiotemporal areal health data within a Bayesian framework using the log-Laplace distribution. A simulation study was conducted to assess the performance of the proposed method and examine whether the model could robustly estimate quantiles of spatiotemporal count data. To perform a test with a real data example, we evaluated the potential application of clustering under the proposed log-Laplace and mean regression. The data were obtained from the total number of emergency room discharges for respiratory conditions, both infectious and non-infectious diseases, in the U.S. state of South Carolina in 2009. From both simulation and case studies, the proposed quantile modeling demonstrated potential for broad applicability in various areas of spatial health studies including anomaly detection. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.
บทคัดย่อ: ไม่พบข้อมูลจากหน่วยงานต้นทาง
ภาษา (EN): en
เอกสารแนบ (EN): https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053727269&doi=10.3390%2fijerph15092042&partnerID=40&md5=b5307900b1469da173dcd9a48dfdf6fa
เผยแพร่โดย (EN): มหาวิทยาลัยมหิดล
คำสำคัญ (EN): Spatio-Temporal Analysis
เจ้าของลิขสิทธิ์ (EN): มหาวิทยาลัยมหิดล
หากไม่พบเอกสารฉบับเต็ม (Full Text) โปรดติดต่อหน่วยงานเจ้าของข้อมูล

การอ้างอิง

Rotejanaprasert C.. "A bayesian quantile modeling for spatiotemporal relative risk: An application to adverse risk detection of respiratory diseases in South Carolina, USA". ไม่ระบุหน่วยงาน. ม.ป.ป.

Rotejanaprasert C.. "A bayesian quantile modeling for spatiotemporal relative risk: An application to adverse risk detection of respiratory diseases in South Carolina, USA". ไม่ระบุหน่วยงาน. ม.ป.ป.. Print.



TARR Wordcloud:
A bayesian quantile modeling for spatiotemporal relative risk: An application to adverse risk detection of respiratory diseases in South Carolina, USA
Rotejanaprasert C.
มหาวิทยาลัยมหิดล
ไม่ระบุวันที่เผยแพร่
Disaster risk reduction and international catastrophe risk insurance facility Modeling environmental impact of service sectors to promote sustainable development of Thailand Break in sedentary behavior reduces the risk of noncommunicable diseases and cardiometabolic risk factors among workers in a petroleum company Application of Light Detection and Ranging (Lidar) Technology for Field Monitoring Climate shocks and migration: an agent-based modeling approach Gis application for landslide risk area assessment in yom watershed, Thailand Bayesian spatial modeling for the joint analysis of zoonosis between human and animal populations Application of molecular technique for risk assessment of Listeria monocytogenes in a frozen cooked chicken processing plant Rational design of novel sirtuin 1 activators via structure-activity insights from application of QSAR modeling Application of environmental DNA analysis for the detection of Opisthorchis viverrini DNA in water samples
คัดลอก URL
กระทู้ของฉัน
ผลการสืบค้นทั้งหมด โพสต์     เรียงลำดับจาก
พัฒนาระบบโดยศูนย์ความรู้เฉพาะด้านวิศวกรรมความรู้และวิศวกรรมภาษาDeveloped by Center of Excellence for Unified Knowledge and Language Engineering
สนับสนุนโดยสำนักงานพัฒนาการวิจัยการเกษตร (องค์การมหาชน)Supported by the Agricultural Research Development Agency (Public Organization)
แบบสำรวจความพึงพอใจQuestionair