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- Birgitte B. Rønn
- Royal Veterinary and Agricultural University , Copenhagen , Denmark
書誌事項
- 公開日
- 2001-07-01
- 権利情報
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- https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model
- DOI
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- 10.1111/1467-9868.00283
- 公開者
- Oxford University Press (OUP)
この論文をさがす
説明
<jats:title>Summary</jats:title> <jats:p>The analysis of a sample of curves can be done by self-modelling regression methods. Within this framework we follow the ideas of nonparametric maximum likelihood estimation known from event history analysis and the counting process set-up. We derive an infinite dimensional score equation and from there we suggest an algorithm to estimate the shape function for a simple shape invariant model. The nonparametric maximum likelihood estimator that we find turns out to be a Nadaraya–Watson-like estimator, but unlike in the usual kernel smoothing situation we do not need to select a bandwidth or even a kernel function, since the score equation automatically selects the shape and the smoothing parameter for the estimation. We apply the method to a sample of electrophoretic spectra to illustrate how it works.</jats:p>
収録刊行物
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- Journal of the Royal Statistical Society Series B: Statistical Methodology
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Journal of the Royal Statistical Society Series B: Statistical Methodology 63 (2), 243-259, 2001-07-01
Oxford University Press (OUP)
