Abstract
We study nonparametric likelihood-based estimators of the mean function of counting processes with panel count data using monotone polynomial splines. The generalized Rosen algorithm, proposed by Zhang Jamshidian (2004), is used to compute the estimators. We show that the proposed spline likelihood-based estimators are consistent and that their rate of convergence can be faster than n1/3. Simulation studies with moderate samples show that the estimators have smaller variances and mean squared errors than their alternatives proposed by Wellner Zhang (2000). A real example from a bladder tumour clinical trial is used to illustrate this method.
| Original language | English |
|---|---|
| Pages (from-to) | 705-718 |
| Number of pages | 14 |
| Journal | Biometrika |
| Volume | 94 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 14 Sept 2007 |
| Externally published | Yes |
Keywords
- Counting process
- Empirical process
- Isotonic regression
- Maximum likelihood estimator
- Maximum pseudolikelihood estimator
- Monotone polynomial spline
- Monte Carlo
ASJC Scopus subject areas
- Statistics and Probability
- General Mathematics
- Agricultural and Biological Sciences (miscellaneous)
- General Agricultural and Biological Sciences
- Statistics, Probability and Uncertainty
- Applied Mathematics