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Non-parametric estimation of spatial variation in relative risk.

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Non-parametric estimation of spatial variation in relative risk. / Kelsall, Julia E.; Diggle, Peter J.

In: Statistics in Medicine, Vol. 14, No. 21-22, 15.11.1995, p. 2335-2342.

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Kelsall, JE & Diggle, PJ 1995, 'Non-parametric estimation of spatial variation in relative risk.', Statistics in Medicine, vol. 14, no. 21-22, pp. 2335-2342. https://doi.org/10.1002/sim.4780142106

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Kelsall, Julia E. ; Diggle, Peter J. / Non-parametric estimation of spatial variation in relative risk. In: Statistics in Medicine. 1995 ; Vol. 14, No. 21-22. pp. 2335-2342.

Bibtex

@article{206ea425ca1440778b1fb70b3124f246,
title = "Non-parametric estimation of spatial variation in relative risk.",
abstract = "We consider the problem of estimating the spatial variation in relative risks of two diseases, say, over a geographical region. Using an underlying Poisson point process model, we approach the problem as one of density ratio estimation implemented with a non-parametric kernel smoothing method. In order to assess the significance of any local peaks or troughs in the estimated risk surface, we introduce pointwise tolerance contours which can enhance a greyscale image plot of the estimate. We also propose a Monte Carlo test of the null hypothesis of constant risk over the whole region, to avoid possible over-interpretation of the estimated risk surface. We illustrate the capabilities of the methodology with two epidemiological examples.",
author = "Kelsall, {Julia E.} and Diggle, {Peter J.}",
year = "1995",
month = nov,
day = "15",
doi = "10.1002/sim.4780142106",
language = "English",
volume = "14",
pages = "2335--2342",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "21-22",

}

RIS

TY - JOUR

T1 - Non-parametric estimation of spatial variation in relative risk.

AU - Kelsall, Julia E.

AU - Diggle, Peter J.

PY - 1995/11/15

Y1 - 1995/11/15

N2 - We consider the problem of estimating the spatial variation in relative risks of two diseases, say, over a geographical region. Using an underlying Poisson point process model, we approach the problem as one of density ratio estimation implemented with a non-parametric kernel smoothing method. In order to assess the significance of any local peaks or troughs in the estimated risk surface, we introduce pointwise tolerance contours which can enhance a greyscale image plot of the estimate. We also propose a Monte Carlo test of the null hypothesis of constant risk over the whole region, to avoid possible over-interpretation of the estimated risk surface. We illustrate the capabilities of the methodology with two epidemiological examples.

AB - We consider the problem of estimating the spatial variation in relative risks of two diseases, say, over a geographical region. Using an underlying Poisson point process model, we approach the problem as one of density ratio estimation implemented with a non-parametric kernel smoothing method. In order to assess the significance of any local peaks or troughs in the estimated risk surface, we introduce pointwise tolerance contours which can enhance a greyscale image plot of the estimate. We also propose a Monte Carlo test of the null hypothesis of constant risk over the whole region, to avoid possible over-interpretation of the estimated risk surface. We illustrate the capabilities of the methodology with two epidemiological examples.

U2 - 10.1002/sim.4780142106

DO - 10.1002/sim.4780142106

M3 - Journal article

VL - 14

SP - 2335

EP - 2342

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 21-22

ER -