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Estimating Relative Risk on the Line Using Nearest Neighbor Statistics

Author

Listed:
  • Dmitri Pavlov

    (Pfizer, Biostatistics)

  • Svetla Slavova

    (University of Kentucky
    University of Kentucky)

  • Richard J. Kryscio

    (University of Kentucky
    University of Kentucky)

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.

Suggested Citation

  • Dmitri Pavlov & Svetla Slavova & Richard J. Kryscio, 2009. "Estimating Relative Risk on the Line Using Nearest Neighbor Statistics," Methodology and Computing in Applied Probability, Springer, vol. 11(2), pages 249-265, June.
    • Handle: RePEc:spr:metcap:v:11:y:2009:i:2:d:10.1007_s11009-007-9039-1
    DOI: 10.1007/s11009-007-9039-1
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    References listed on IDEAS

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    1. Mack, Y. P. & Rosenblatt, M., 1979. "Multivariate k-nearest neighbor density estimates," Journal of Multivariate Analysis, Elsevier, vol. 9(1), pages 1-15, March.
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