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Accepted (AM)Open Access (License Unspecified), Open
Journal article
Estimation in partially linear models and numerical comparisons
Computational statistics & data analysis, v 50(3), pp 675-687
2006
PMID: 20174596
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Partially linear models with local kernel regression are popular nonparametric techniques. However, bandwidth selection in the models is a puzzling topic that has been addressed in the literature with the use of undersmoothing and regular smoothing. In an attempt to address the strategy of bandwidth selection, we review profile-kernel based and backfitting methods for partially linear models, and justify why undersmoothing is necessary for backfitting method and why the “optimal” bandwidth works out for profile-kernel based method. We suggest a general computation strategy for estimating nonparametric function. We also employ the penalized spline method for partially linear models and conduct intensive simulation experiments to explore the numerical performance of the penalized spline method, profile and backfitting methods. A real dataset is analyzed with the three methods.
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Details
- Title
- Estimation in partially linear models and numerical comparisons
- Creators
- Hua Liang - St. Jude Children's Research Hospital
- Publication Details
- Computational statistics & data analysis, v 50(3), pp 675-687
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000232738400006
- Scopus ID
- 2-s2.0-25444455666
- Other Identifier
- 991019320712504721
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- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Statistics & Probability