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

Producción científica: Article › revisión exhaustiva

Resumen

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.

Idioma original	English
Páginas (desde-hasta)	249-265
Número de páginas	17
Publicación	Methodology and Computing in Applied Probability
Volumen	11
N.º	2 SPEC. ISS.
DOI	https://doi.org/10.1007/s11009-007-9039-1
Estado	Published - jun 2009

Nota bibliográfica

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.

Financiación

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.

Financiadores
National Institute for Occupational Safety and Health

ASJC Scopus subject areas

Statistics and Probability
General Mathematics

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10.1007/s11009-007-9039-1

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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.",

keywords = "Incomplete beta approximation, Motor vehicle crashes, Permutation tests, Relative risk function, kNN density estimator",

author = "Dmitri Pavlov and Svetla Slavova and Kryscio, \{Richard J.\}",

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.",

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AU - Slavova, Svetla

AU - Kryscio, Richard J.

N1 - 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.

PY - 2009/6

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N2 - 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.

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.

KW - Incomplete beta approximation

KW - Motor vehicle crashes

KW - Permutation tests

KW - Relative risk function

KW - kNN density estimator

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U2 - 10.1007/s11009-007-9039-1

DO - 10.1007/s11009-007-9039-1

M3 - Article

AN - SCOPUS:63549132656

SN - 1387-5841

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SP - 249

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JO - Methodology and Computing in Applied Probability

JF - Methodology and Computing in Applied Probability

IS - 2 SPEC. ISS.

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Estimating relative risk on the line using nearest neighbor statistics

Resumen

Nota bibliográfica

Financiación

ASJC Scopus subject areas

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