Skip to main navigation
Skip to search
Skip to main content
Experts@Minnesota Home
Home
Profiles
Research units
University Assets
Projects and Grants
Research output
Press/Media
Datasets
Activities
Fellowships, Honors, and Prizes
Search by expertise, name or affiliation
Regression models for public health surveillance data: A simulation study
Hyun Kim
, D. Kriebel
Environmental Health Sciences
Research output
:
Contribution to journal
›
Article
›
peer-review
13
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Regression models for public health surveillance data: A simulation study'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Medicine & Life Sciences
Public Health Surveillance
100%
Statistical Models
78%
Wounds and Injuries
33%
Monte Carlo Method
33%
Confidence Intervals
27%
Datasets
18%
Epidemiology
18%
Data Analysis
17%
Research Personnel
16%
Social Sciences
surveillance
60%
simulation
54%
public health
50%
regression
44%
trend
15%
confidence
11%
epidemiology
7%
indication
6%
determinants
5%
performance
3%