Journal article
Efficient estimation of the partly linear additive Cox model
The Annals of statistics, Vol.27(5), pp.1536-1563
10/01/1999
DOI: 10.1214/aos/1017939141
Abstract
The partly linear additive Cox model is an extension of the (linear) Cox model and allows flexible modeling of covariate effects semiparametrically. We study asymptotic properties of the maximum partial likelihood estimator of this model with right-censored data using polynomial splines. We show that, with a range of choices of the smoothing parameter (the number of spline basis functions) required for estimation of the nonparametric components, the estimator of the finite-dimensional regression parameter is root-n consistent, asymptotically normal and achieves the semiparametric information bound. Rates of convergence for the estimators of the nonparametric components are obtained. They are comparable to the rates in nonparametric regression. Implementation of the estimation approach can be done easily and is illustrated by using a simulated example.
Details
- Title: Subtitle
- Efficient estimation of the partly linear additive Cox model
- Creators
- Jian Huang
- Resource Type
- Journal article
- Publication Details
- The Annals of statistics, Vol.27(5), pp.1536-1563
- DOI
- 10.1214/aos/1017939141
- ISSN
- 0090-5364
- eISSN
- 2168-8966
- Language
- English
- Date published
- 10/01/1999
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257619102771
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