Local linear additive quantile regression
We consider non-parametric additive quantile regression estimation by kernel-weighted local linear fitting. The estimator is based on localizing the characterization of quantile regression as the minimizer of the appropriate 'check function'. A backfitting algorithm and aheuristic rule for...
| Main Authors: | , |
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| Format: | Journal Article |
| Published: |
Blackwell Publishing Ltd
2004
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| Online Access: | http://hdl.handle.net/20.500.11937/30782 |
| _version_ | 1848753188250845184 |
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| author | Yu, K. Lu, Zudi |
| author_facet | Yu, K. Lu, Zudi |
| author_sort | Yu, K. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We consider non-parametric additive quantile regression estimation by kernel-weighted local linear fitting. The estimator is based on localizing the characterization of quantile regression as the minimizer of the appropriate 'check function'. A backfitting algorithm and aheuristic rule for selecting the smoothing parameter are explored. We also study the estimation of average-derivative quantile regression under the additive model. The techniques are illustrated by a simulated example and a real data set. |
| first_indexed | 2025-11-14T08:20:32Z |
| format | Journal Article |
| id | curtin-20.500.11937-30782 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:20:32Z |
| publishDate | 2004 |
| publisher | Blackwell Publishing Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-307822017-09-13T15:55:04Z Local linear additive quantile regression Yu, K. Lu, Zudi quantile regression additive models average derivative local linear fitting bandwidth selection backfitting algorithm We consider non-parametric additive quantile regression estimation by kernel-weighted local linear fitting. The estimator is based on localizing the characterization of quantile regression as the minimizer of the appropriate 'check function'. A backfitting algorithm and aheuristic rule for selecting the smoothing parameter are explored. We also study the estimation of average-derivative quantile regression under the additive model. The techniques are illustrated by a simulated example and a real data set. 2004 Journal Article http://hdl.handle.net/20.500.11937/30782 10.1111/j.1467-9469.2004.03_035.x Blackwell Publishing Ltd restricted |
| spellingShingle | quantile regression additive models average derivative local linear fitting bandwidth selection backfitting algorithm Yu, K. Lu, Zudi Local linear additive quantile regression |
| title | Local linear additive quantile regression |
| title_full | Local linear additive quantile regression |
| title_fullStr | Local linear additive quantile regression |
| title_full_unstemmed | Local linear additive quantile regression |
| title_short | Local linear additive quantile regression |
| title_sort | local linear additive quantile regression |
| topic | quantile regression additive models average derivative local linear fitting bandwidth selection backfitting algorithm |
| url | http://hdl.handle.net/20.500.11937/30782 |