scholarly journals Penalized log-likelihood estimation for partly linear transformation models with current status data

2005 ◽  
Vol 33 (5) ◽  
pp. 2256-2290 ◽  
Author(s):  
Shuangge Ma ◽  
Michael R. Kosorok
Keyword(s):  
Linear Transformation ◽  
Likelihood Estimation ◽  
Current Status Data ◽  
Current Status ◽  
Transformation Models ◽  
Linear Transformation Models ◽  
Log Likelihood ◽  
Status Data

PENALIZED LOG-LIKELIHOOD ESTIMATION FOR COX-FRAILTY MODEL WITH NONINFORMATIVE BIVARIATE CURRENT STATUS DATA

2021 ◽  
pp. 16-27
Author(s):  
Alhassan Faisal
Keyword(s):  
Variance Estimation ◽  
Proportional Hazards ◽  
Likelihood Estimation ◽  
Frailty Model ◽  
Cox Proportional Hazards ◽  
Current Status Data ◽  
Current Status ◽  
Hazard Functions ◽  
Failure Times ◽  
Status Data

A Penalized Maximum Likelihood Estimation (PMLE) procedure is proposed for Cox proportional hazards frailty model with noninformative bivariate current status data. An integrated splines (I-splines) was used to approximate the two unknown baseline cumulative hazard functions of the failure times. The one-parameter gamma frailty distribution was used to model the correlation between the two failure times. An easy to implement computational algorithm is proposed to estimate the regression and splines parameters. Bayesian technique as proposed by Wahba (1983) was employed for the variance estimation. The statistical properties of the estimated parameters were studied through extensive simulation and it was observed that the PMLEs were consistent, asymptotically normal and efcient. In addition, the estimators were robust to the choice of knots, censoring rates and type of frailty distribution used. The proposed methodology is further demonstrated through the analysis of the tumorigenicity experiment data by Lindsey and Ryan (1994).

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 transformation models for current status data with informative censoring

2012 ◽  
Vol 54 (5) ◽  
pp. 641-656 ◽  
Author(s):  
Chyong-Mei Chen ◽  
Tai-Fang C. Lu ◽  
Man-Hua Chen ◽  
Chao-Min Hsu
Keyword(s):  
Informative Censoring ◽  
Current Status Data ◽  
Current Status ◽  
Transformation Models ◽  
Semiparametric Transformation ◽  
Status Data
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