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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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
2013
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/97695 http://hdl.handle.net/10220/17096 |
| الوسوم: |
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| المؤسسة: | Nanyang Technological University |
| اللغة: | English |
| الملخص: | 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. |
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