Efficient estimation of semiparametric varying-coefficient partially linear transformation model with current status data

2021 ◽  
pp. 1-20
Author(s):  
Riyadh Al-Mosawi ◽  
Xuewen Lu
Keyword(s):  
Linear Transformation ◽  
Efficient Estimation ◽  
Current Status Data ◽  
Transformation Model ◽  
Current Status ◽  
Varying Coefficient ◽  
Linear Transformation Model ◽  
Partially Linear ◽  
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.

SEMIPARAMETRIC ESTIMATION OF PARTIALLY LINEAR TRANSFORMATION MODELS UNDER CONDITIONAL QUANTILE RESTRICTION

2014 ◽  
Vol 32 (2) ◽  
pp. 458-497 ◽  
Author(s):  
Zhengyu Zhang
Keyword(s):  
Linear Transformation ◽  
Semiparametric Estimation ◽  
Transformation Model ◽  
Dimensional Parameter ◽  
Conditional Quantile ◽  
Infinite Dimensional ◽  
Linear Transformation Model ◽  
Partially Linear ◽  
Linear Transformation Models ◽  
Image Position

This article is concerned with semiparametric estimation of a partially linear transformation model under conditional quantile restriction with no parametric restriction imposed either on the link functional form or on the error term distribution. We describe for the finite-dimensional parameter a$\sqrt n$-consistent estimator which combines the features of Chen (2010)’s maximum integrated score estimator as well as Lee (2003)’s average quantile regression. We show the remaining two infinite-dimensional unknown functions in the model can be separately identified and propose estimators for these functions based on the marginal integration method. Furthermore, a simple approach is proposed to estimate the average partial quantile effect. Two important extensions, i.e., random censoring as well as estimating a transformation model with an endogenous regressor are also considered.

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