Comparing nonparametric versus parametric regression fits
In general, there will be visible differences between a parametric and a nonparametric curve estimate. It is therefore quite natural to compare these in order to decide whether the parametric model could be justified. An asymptotic quantification is the distribution of the integrated squared differe...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
1993
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| In: |
The annals of statistics
Year: 1993, Jahrgang: 21, Heft: 4, Pages: 1926-1947 |
| ISSN: | 2168-8966 |
| DOI: | 10.1214/aos/1176349403 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1214/aos/1176349403 Verlag, Volltext: https://projecteuclid.org/euclid.aos/1176349403 Verlag, Volltext: https://projecteuclid.org/download/pdf_1/euclid.aos/1176349403 
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| Verfasserangaben: | W. Härdle, E. Mammen |
MARC
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| 520 | |a In general, there will be visible differences between a parametric and a nonparametric curve estimate. It is therefore quite natural to compare these in order to decide whether the parametric model could be justified. An asymptotic quantification is the distribution of the integrated squared difference between these curves. We show that the standard way of bootstrapping this statistic fails. We use and analyse a different form of bootstrapping for this task. We call this method the wild bootstrap and apply it to fitting Engel curves in expenditure data analysis. | ||
| 650 | 4 | |a wild bootstrap | |
| 650 | 4 | |a bootstrap | |
| 650 | 4 | |a goodness-of-fit test | |
| 650 | 4 | |a Kernel estimate | |
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