Abstract
We propose a flexible and computationally efficient penalized estimation method for a semi-parametric linear transformation model with current status data. To facilitate model fitting, the unknown monotone function is approximated by monotone B-splines, and a computationally efficient hybrid algorithm involving the Fisher scoring algorithm and the isotonic regression is developed. A goodness-of-fit test and model diagnostics are also considered. The asymptotic properties of the penalized estimators are established, including the optimal rate of convergence for the function estimator and the semi-parametric efficiency for the regression parameter estimators. An extensive numerical experiment is conducted to evaluate the finite-sample properties of the penalized estimators, and the methodology is further illustrated with two real studies.
Original language | English (US) |
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Journal | Statistical Methods in Medical Research |
DOIs | |
State | Accepted/In press - Jan 1 2018 |
Externally published | Yes |
Keywords
- Current status data
- efficient estimation
- goodness-of-fit
- penalized spline
- transformation model
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability
- Health Information Management