Journal article
Semiparametric Estimation Methods for Panel Count Data Using Monotone B-Splines
Journal of the American Statistical Association, Vol.104(487), pp.1060-1070
09/01/2009
DOI: 10.1198/jasa.2009.tm08086
Abstract
We study semiparametric likelihood-based methods for panel count data with proportional mean model E[ℕ(t)|Z]=Λ 0 (t)exp(β 0 T Z), where Z is a vector of covariates and Λ 0 (t) is the baseline mean function. We propose to estimate Λ 0 (t) and β 0 jointly with Λ 0 (t) approximated by monotone B-splines and to compute the estimators using generalized Rosen algorithm proposed by Jamshidian (2004). We show that the proposed spline-based likelihood estimators of Λ 0 (t) are consistent with a possibly better than n 1/3 convergence rate if Λ 0 (t) is sufficiently smooth. The normality of the estimators of β 0 is also established. Comparisons between the proposed estimators and their alternatives studied in Wellner and Zhang (2007) are made through simulations studies, regarding their finite sample performance and computational complexity. A real example from a bladder tumor clinical trial is used to illustrate the methods.
Details
- Title: Subtitle
- Semiparametric Estimation Methods for Panel Count Data Using Monotone B-Splines
- Creators
- Minggen LuYing ZhangJian Huang
- Resource Type
- Journal article
- Publication Details
- Journal of the American Statistical Association, Vol.104(487), pp.1060-1070
- DOI
- 10.1198/jasa.2009.tm08086
- ISSN
- 0162-1459
- eISSN
- 1537-274X
- Publisher
- Taylor & Francis
- Language
- English
- Date published
- 09/01/2009
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9983985821302771
Metrics
14 Record Views