The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Tuning parameter selectors for the smoothly clipped absolute deviation method
Open accessAbstract
The penalised least squares approach with smoothly clipped absolute deviation penalty has been consistently demonstrated to be an attractive regression shrinkage and selection method. It not only automatically and consistently selects the important variables, but also produces estimators which are as efficient as the oracle estimator. However, these attractive features depend on appropriately choosing the tuning parameter. We show that the commonly used the generalised crossvalidation cannot select the tuning parameter satisfactorily, with a nonignorable overfitting effect in the resulting model. In addition, we propose a bic tuning parameter selector, which is shown to be able to identify the true model consistently. Simulation studies are presented to support theoretical findings, and an empirical example is given to illustrate its use in the Female Labor Supply data.
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Cites
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Self-citationCraven, P., & Wahba, G. (1978) · Numerische Mathematik · doi:10.1007/bf01404567 · Cited by 3,388
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Sure Independence Screening for Ultrahigh Dimensional Feature Space
Fan, J., & Lv, J. (2008) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2008.00674.x · Cited by 2,711
On the adaptive elastic-net with a diverging number of parameters
Zou, H., & Zhang, H. H. (2009) · The Annals of Statistics · doi:10.1214/08-aos625 · Cited by 847
Sensitivity and specificity of information criteria
(2019) · doi:10.1093/bib/bbz016 · Cited by 718
TENET: Tail-Event driven NETwork risk
Härdle, W. K., Wang, W., & Yu, L. (2016) · Journal of Econometrics · doi:10.1016/j.jeconom.2016.02.013 · Cited by 470
Kai, B., Li, R., & Zou, H. (2011) · The Annals of Statistics · doi:10.1214/10-aos842 · Cited by 301
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Metadata
- Authors
- Wang, H., Li, R., & Tsai, C.-L.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-08-05
- Volume
- 94
- Issue
- 3
- Pages
- 553-568
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2086205459
- Cited by
- 768
- References
- 19
- FWCI
- 19.82
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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