• 174 Citations
  • 9 h-Index
20092019
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Fingerprint Dive into the research topics where Jungsoon Choi is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Covariates Mathematics
Health Mathematics
Space-time Mathematics
Mixture Model Mathematics
Low Birth Weight Infant Medicine & Life Sciences
Variable Selection Mathematics
Varying Coefficients Mathematics
Platelet Aggregation Inhibitors Medicine & Life Sciences

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Research Output 2009 2019

  • 174 Citations
  • 9 h-Index
  • 25 Article
  • 1 Chapter
1 Citation (Scopus)

A Bayesian two-stage spatially dependent variable selection model for space–time health data

Choi, J. & Lawson, A. B., 2019 Sep 1, In : Statistical Methods in Medical Research. 28, 9, p. 2570-2582 13 p.

Research output: Contribution to journalArticleResearchpeer-review

Selection Model
Variable Selection
Health
Space-time
Risk Factors

Exploring the catchment area of an urban railway station by using transit card data: Case study in Seoul

Eom, J. K., Choi, J., Park, M. S. & Heo, T. Y., 2019 Dec 1, In : Cities. 95, 102364.

Research output: Contribution to journalArticleResearchpeer-review

German Federal Railways
pricing
land use
data mining
time of day

Space–time relationship between short-term exposure to fine and coarse particles and mortality in a nationwide analysis of Korea: A bayesian hierarchical spatio-temporal model

Kang, D., Jang, Y., Choi, H., Hwang, S. S., Koo, Y. & Choi, J., 2019 Jun 2, In : International Journal of Environmental Research and Public Health. 16, 12, 2111.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
Korea
Air Pollution
Republic of Korea
Mortality
Particulate Matter
1 Citation (Scopus)

Bayesian spatially dependent variable selection for small area health modeling

Choi, J. & Lawson, A. B., 2018 Jan 1, In : Statistical Methods in Medical Research. 27, 1, p. 234-249 16 p.

Research output: Contribution to journalArticleResearchpeer-review

Variable Selection
Covariates
Health
Dependent
Modeling

Bayesian zero-inflated spatio-temporal modelling of scrub typhus data in Korea, 2010-2014

Kang, D. & Choi, J., 2018 Nov 13, In : Geospatial Health. 13, 2, p. 215-223 9 p.

Research output: Contribution to journalArticleResearchpeer-review

Scrub Typhus
scrub
Korea
modeling
Weather