Correlation and marginal longitudinal kernel nonparametric regression
We consider nonparametric regression in a marginal longitudinal data framework. Previous work ([3]) has shown that the kernel nonparametric regression methods extant in the literature for such correlated data have the discouraging property that they generally do not improve upon methods that ignore...
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| Main Authors: | , , , |
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| Format: | Chapter/Article Conference Paper |
| Language: | English |
| Published: |
2004
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| In: |
Proceedings of the Second Seattle Symposium in Biostatistics
Year: 2004, Pages: 23-33 |
| Online Access: | Verlag, Volltext: https://link.springer.com/chapter/10.1007/978-1-4419-9076-1_2
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| Author Notes: | Oliver B. Linton, Enno Mammen, Xihong Lin, Raymond J. Carroll |
| Summary: | We consider nonparametric regression in a marginal longitudinal data framework. Previous work ([3]) has shown that the kernel nonparametric regression methods extant in the literature for such correlated data have the discouraging property that they generally do not improve upon methods that ignore the correlation structure entirely. The latter methods are called working independence methods. We construct a two- stage kernel-based estimator that asymptotically uniformly improves upon the working independence estimator. A small simulation study is given in support of the asymptotics. |
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| Item Description: | Gesehen am 05.02.2018 |
| Physical Description: | Online Resource |
| ISBN: | 9781441990761 |