Abstract
This paper considers a non-parametric method for identifying intervals on the line where the relative risk of cases to controls exceeds a pre-specified level. The method is based on the k th nearest neighbor (kNN) approach for density estimation. An asymptotic result is presented that yields an explicit formula for constructing a confidence interval for the relative risk at a given point. Numerical simulations are used to compare this approach with a kernel density estimation procedure. An application is made to a case-control study in which the relative risk of motor vehicle crashes caused by female drivers is compared to male drivers in the state of Kentucky as a function of age and then by time of day.
| Original language | English |
|---|---|
| Pages (from-to) | 249-265 |
| Number of pages | 17 |
| Journal | Methodology and Computing in Applied Probability |
| Volume | 11 |
| Issue number | 2 SPEC. ISS. |
| DOIs | |
| State | Published - Jun 2009 |
Bibliographical note
Funding Information:Acknowledgements The collision data were provided by Dr. Terry Bunn (Kentucky Injury Prevention and Research Center, University of Kentucky). This work was partially supported by Grant/Cooperative Agreement Number 1U60OH008483-01 from The National Institute for Occupational Safety and Health (NIOSH). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIOSH.
Keywords
- Incomplete beta approximation
- Motor vehicle crashes
- Permutation tests
- Relative risk function
- kNN density estimator
ASJC Scopus subject areas
- Statistics and Probability
- Mathematics (all)