Covariate selection for semiparametric hazard function regression models
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 186-204 |
| Journal / Publication | Journal of Multivariate Analysis |
| Volume | 92 |
| Issue number | 1 |
| Publication status | Published - Jan 2005 |
| Externally published | Yes |
Link(s)
Abstract
We study a flexible class of nonproportional hazard function regression models in which the influence of the covariates splits into the sum of a parametric part and a time-dependent nonparametric part. We develop a method of covariate selection for the parametric part by adjusting for the implicit fitting of the nonparametric part. Asymptotic consistency of the proposed covariate selection method is established, leading to asymptotically normal estimators of both parametric and nonparametric parts of the model in the presence of covariate selection. The approach is applied to a real data set and a simulation study is presented. © 2003 Elsevier Inc. All rights reserved.
Research Area(s)
- Additive risk model, Cox model, Model selection, Penalized likelihood, Penalized partial likelihood, Survival analysis
Bibliographic Note
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Citation Format(s)
Covariate selection for semiparametric hazard function regression models. / Bunea, Florentina; McKeague, Ian W.
In: Journal of Multivariate Analysis, Vol. 92, No. 1, 01.2005, p. 186-204.
In: Journal of Multivariate Analysis, Vol. 92, No. 1, 01.2005, p. 186-204.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
