Variable selection in high-dimensional partly linear additive models
Semiparametric models are particularly useful for high-dimensional regression problems. In this paper, we focus on partly linear additive models with a large number of predictors (can be larger than the sample size) and consider model estimation and variable selection based on polynomial spline expa...
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| Format: | Journal Article |
| Language: | English |
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2013
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| Online Access: | https://hdl.handle.net/10356/97695 http://hdl.handle.net/10220/17096 |
| _version_ | 1826129320778661888 |
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| author | Lian, Heng |
| author2 | School of Physical and Mathematical Sciences |
| author_facet | School of Physical and Mathematical Sciences Lian, Heng |
| author_sort | Lian, Heng |
| collection | NTU |
| description | Semiparametric models are particularly useful for high-dimensional regression problems. In this paper, we focus on partly linear additive models with a large number of predictors (can be larger than the sample size) and consider model estimation and variable selection based on polynomial spline expansion for the nonparametric part with adaptive lasso penalty on the linear part. Convergence rates as well as asymptotic normality of the linear part are shown. We also perform some Monte Carlo studies to demonstrate the performance of the estimator. |
| first_indexed | 2024-10-01T07:38:40Z |
| format | Journal Article |
| id | ntu-10356/97695 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:38:40Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/976952020-03-07T12:31:33Z Variable selection in high-dimensional partly linear additive models Lian, Heng School of Physical and Mathematical Sciences DRNTU::Science::Mathematics::Statistics Semiparametric models are particularly useful for high-dimensional regression problems. In this paper, we focus on partly linear additive models with a large number of predictors (can be larger than the sample size) and consider model estimation and variable selection based on polynomial spline expansion for the nonparametric part with adaptive lasso penalty on the linear part. Convergence rates as well as asymptotic normality of the linear part are shown. We also perform some Monte Carlo studies to demonstrate the performance of the estimator. 2013-10-31T01:43:18Z 2019-12-06T19:45:33Z 2013-10-31T01:43:18Z 2019-12-06T19:45:33Z 2012 2012 Journal Article Lian, H. (2012). Variable selection in high-dimensional partly linear additive models. Journal of nonparametric statistics, 24(4), 825-839. https://hdl.handle.net/10356/97695 http://hdl.handle.net/10220/17096 10.1080/10485252.2012.701300 en Journal of nonparametric statistics |
| spellingShingle | DRNTU::Science::Mathematics::Statistics Lian, Heng Variable selection in high-dimensional partly linear additive models |
| title | Variable selection in high-dimensional partly linear additive models |
| title_full | Variable selection in high-dimensional partly linear additive models |
| title_fullStr | Variable selection in high-dimensional partly linear additive models |
| title_full_unstemmed | Variable selection in high-dimensional partly linear additive models |
| title_short | Variable selection in high-dimensional partly linear additive models |
| title_sort | variable selection in high dimensional partly linear additive models |
| topic | DRNTU::Science::Mathematics::Statistics |
| url | https://hdl.handle.net/10356/97695 http://hdl.handle.net/10220/17096 |
| work_keys_str_mv | AT lianheng variableselectioninhighdimensionalpartlylinearadditivemodels |