Smooth backfitting of proportional hazards: anew approach projecting survival data
Smooth backfitting has proven to have a number of theoretical and practical advantages in structured regression. Smooth backfitting projects the data down onto the structured space of interest providing a direct link between data and estimator. This paper introduces the ideas of smooth backfitting t...
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| Main Authors: | , , , |
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| Format: | Article (Journal) Chapter/Article |
| Language: | English |
| Published: |
14 July 2017
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| In: |
Arxiv
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| Online Access: | Verlag, kostenfrei, Volltext: http://arxiv.org/abs/1707.04622 Verlag, kostenfrei, Volltext: https://arxiv.org/pdf/1707.04622.pdf 
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| Author Notes: | Munir Hiabu, Enno Mammen, Maria Dolores Martinez-Miranda, Jens Perch Nielsen |
| Summary: | Smooth backfitting has proven to have a number of theoretical and practical advantages in structured regression. Smooth backfitting projects the data down onto the structured space of interest providing a direct link between data and estimator. This paper introduces the ideas of smooth backfitting to survival analysis in a proportional hazard model, where we assume an underlying conditional hazard with multiplicative components. We develop asymptotic theory for the estimator and we use the smooth backfitter in a practical application, where we extend recent advances of in-sample forecasting methodology by allowing more information to be incorporated, while still obeying the structured requirements of in-sample forecasting. |
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| Item Description: | Gesehen am 22.01.2018 |
| Physical Description: | Online Resource |