scholarly journals A penalized likelihood approach for efficiently estimating a partially linear additive transformation model with current status data

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Yan Liu ◽  
Minggen Lu ◽  
Christopher S. McMahan
Keyword(s):  
Penalized Likelihood ◽  
Current Status Data ◽  
Transformation Model ◽  
Current Status ◽  
Partially Linear ◽  
Likelihood Approach ◽  
Penalized Likelihood Approach ◽  
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.

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