scholarly journals Semiparametric additive transformation model under current status data

2011 ◽  
Vol 5 (0) ◽  
pp. 1735-1764 ◽  
Author(s):  
Guang Cheng ◽  
Xiao Wang
Keyword(s):  
Current Status Data ◽  
Transformation Model ◽  
Current Status ◽  
Status Data

Efficient estimation of a linear transformation model for current status data via penalized splines

2018 ◽  
Vol 29 (1) ◽  
pp. 3-14
Author(s):  
Minggen Lu ◽  
Yan Liu ◽  
Chin-Shang Li
Keyword(s):  
Linear Transformation ◽  
Penalized Splines ◽  
Efficient Estimation ◽  
Current Status Data ◽  
Transformation Model ◽  
Current Status ◽  
Finite Sample ◽  
Computationally Efficient ◽  
Linear Transformation Model ◽  
Status Data

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.

Regression analysis of informative current status data with the additive hazards model

2014 ◽  
Vol 21 (2) ◽  
pp. 241-258 ◽  
Author(s):  
Shishun Zhao ◽  
Tao Hu ◽  
Ling Ma ◽  
Peijie Wang ◽  
Jianguo Sun
Keyword(s):  
Regression Analysis ◽  
Current Status Data ◽  
Current Status ◽  
Additive Hazards Model ◽  
Additive Hazards ◽  
Hazards Model ◽  
Status Data
Sign in / Sign up

Export Citation Format

Share Document