Estimating relative risk on the line using nearest neighbor statistics

Dmitri Pavlov; Svetla Slavova; Richard J. Kryscio

doi:10.1007/s11009-007-9039-1

Estimating relative risk on the line using nearest neighbor statistics

Dmitri Pavlov, Svetla Slavova, Richard J. Kryscio

Research output: Contribution to journal › Article › peer-review

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	https://doi.org/10.1007/s11009-007-9039-1
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.

Funding

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.

Funders	Funder number
National Institute for Occupational Safety and Health

Keywords

Incomplete beta approximation
Motor vehicle crashes
Permutation tests
Relative risk function
kNN density estimator

ASJC Scopus subject areas

Statistics and Probability
General Mathematics

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10.1007/s11009-007-9039-1

Cite this

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title = "Estimating relative risk on the line using nearest neighbor statistics",

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.",

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AB - 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.

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KW - Permutation tests

KW - Relative risk function

KW - kNN density estimator

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Estimating relative risk on the line using nearest neighbor statistics

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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