Abstract
We study semiparametric likelihood-based methods for panel count data with proportional mean model E[ℕ (t)|Z] = Λ0(t) exp(βT0Z), where Z is a vector of covariates and Λ0(t) is the baseline mean function. We propose to estimate Λ0(t) and β0jointly with Λ0(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 Λ0(t) are consistent with a possibly better than n1/3convergence rate if Λ0(t) is sufficiently smooth. The normality of the estimators of β0is 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.
Original language | English |
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Pages (from-to) | 1060-1070 |
Number of pages | 11 |
Journal | Journal of the American Statistical Association |
Volume | 104 |
Issue number | 487 |
DOIs | |
Publication status | Published - 14 Oct 2009 |
Externally published | Yes |
Keywords
- B-splines
- Counting process
- Empirical process
- Generalized rosen algorithm
- Maximum likelihood method
- Maximum pseudolikelihood method
- Monte Carlo
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty