Generated covariates in nonparametric estimation: a short review
In many applications, covariates are not observed but have to be estimated from data. We outline some regression-type models where such a situation occurs and discuss estimation of the regression function in this context.We review theoretical results on how asymptotic properties of nonparametric est...
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| Main Authors: | , , |
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| Format: | Book/Monograph Working Paper |
| Language: | English |
| Published: |
Berlin
SFB 649, Economic Risk
2012
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| Series: | SFB 649 discussion paper
2012-042 |
| In: |
SFB 649 discussion paper (2012,42)
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| Subjects: | |
| Online Access: | Verlag, Volltext: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2012-042.pdf Download aus dem Internet, Stand: 27.06.2012, Volltext: http://hdl.handle.net/10419/79567 
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| Author Notes: | Enno Mammen; Christoph Rothe; Melanie Schienle |
MARC
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| 520 | |a In many applications, covariates are not observed but have to be estimated from data. We outline some regression-type models where such a situation occurs and discuss estimation of the regression function in this context.We review theoretical results on how asymptotic properties of nonparametric estimators differ in the presence of generated covariates from the standard case where all covariates are observed. These results also extend to settings where the focus of interest is on average functionals of the regression function. -- Nonparametric estimation ; generated covariates | ||
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