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Estimating Relative Risk on the Line Using Nearest Neighbor Statistics

Dmitri Pavlov, Svetla Slavova and Richard J. Kryscio ()
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Dmitri Pavlov: Pfizer, Biostatistics
Svetla Slavova: University of Kentucky
Richard J. Kryscio: University of Kentucky

Methodology and Computing in Applied Probability, 2009, vol. 11, issue 2, 249-265

Abstract: 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 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.

Keywords: kNN density estimator; Relative risk function; Permutation tests; Incomplete beta approximation; Motor vehicle crashes; 62605 (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1007/s11009-007-9039-1

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