Variable selection in a partially linear proportional hazards model with a diverging dimensionality
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 61-69 |
Journal / Publication | Statistics and Probability Letters |
Volume | 83 |
Issue number | 1 |
Publication status | Published - Jan 2013 |
Externally published | Yes |
Link(s)
Abstract
We consider the problem of simultaneous variable selection and estimation in partially linear proportional hazards models when the number of covariates in the linear part diverges with the sample size. We apply the smoothly clipped absolute deviation (SCAD) penalty to select the significant covariates in the linear part. Some simulations and a real data set are presented. © 2012 Elsevier B.V.
Research Area(s)
- Akaike information criterion (AIC), Bayesian information criterion (BIC), Cross-validation, Partial likelihood, SCAD
Citation Format(s)
Variable selection in a partially linear proportional hazards model with a diverging dimensionality. / Hu, Yuao; Lian, Heng.
In: Statistics and Probability Letters, Vol. 83, No. 1, 01.2013, p. 61-69.
In: Statistics and Probability Letters, Vol. 83, No. 1, 01.2013, p. 61-69.
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review