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 es...
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| Main Authors: | , , |
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| Format: | Chapter/Article Conference Paper |
| Language: | English |
| Published: |
2013
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| In: |
Recent Developments in Modeling and Applications in Statistics
Year: 2012, Pages: 97-105 |
| Online Access: | Verlag, Volltext: https://link.springer.com/chapter/10.1007/978-3-642-32419-2_11
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| Author Notes: | Enno Mammen, Christoph Rothe, Melanie Schienle |
| Summary: | 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. |
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| Item Description: | First online: 13 September 2012 Gesehen am 30.01.2018 |
| Physical Description: | Online Resource |
| ISBN: | 9783642324192 |