Estimating Relative Risk on the Line Using Nearest Neighbor Statistics

摘要

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 kth 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.