Kernel estimation of relative risk.

Kelsall, Julia E. and Diggle, Peter J. (1995) Kernel estimation of relative risk. Bernoulli, 1 (1-2). pp. 3-16. ISSN 1350-7265

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Abstract

Estimation of a relative risk function using a ratio of two kernel density estimates is considered, concentrating on the problem of choosing the smoothing parameters. A cross-validation method is proposed, compared with a range of other methods and found to be an improvement when the actual risk is close to constant. In particular, theoretical and empirical comparisons demonstrate the advantage of choosing the smoothing parameters jointly. The methodology was motivated by a class of problems in environmental epidemiology, and an application in this area is described.

Item Type:
Journal Article
Journal or Publication Title:
Bernoulli
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2613
Subjects:
?? cross-validationepidemiologykernel density estimationsmoothing parametersstatistics and probabilityqa mathematics ??
ID Code:
19573
Deposited By:
Deposited On:
11 Nov 2008 09:08
Refereed?:
Yes
Published?:
Published
Last Modified:
15 Jul 2024 09:45