scholarly journals Sieve Estimation of a Class of Partially Linear Transformation Models With Interval-Censored Competing Risks Data

2023 ◽  
Author(s):  
Xuewen Lu ◽  
Yan Wang ◽  
Dipankar Bandyopadhyay ◽  
Giorgos Bakoyannis
Keyword(s):  
Competing Risks ◽  
Linear Transformation ◽  
Transformation Models ◽  
Partially Linear ◽  
Sieve Estimation ◽  
Linear Transformation Models ◽  
Interval Censored ◽  
Competing Risks Data

Linear transformation models for interval-censored data

2009 ◽  
Vol 9 (4) ◽  
pp. 321-343 ◽  
Author(s):  
Zhigang Zhang
Keyword(s):  
Statistical Analysis ◽  
Censored Data ◽  
Linear Transformation ◽  
Graphical Model ◽  
Real Life ◽  
Random Variable ◽  
Interval Censored Data ◽  
Transformation Models ◽  
Linear Transformation Models ◽  
Interval Censored

In statistical analysis, when the value of a random variable is only known to be between two bounds, we say that this random variable is interval censored. This complicated censoring pattern is a common problem in research fields such as clinical trials or actuarial studies and raises challenges for statistical analysis. In this paper, we focus on regression analysis of case 2 interval-censored data. We first briefly review existing regression methods and an estimation approach under the class of linear transformation models developed by Zhang et al. We then propose a method for survival probability prediction via generalized estimating equations. We also consider a graphical model checking technique and a model selection tool. Some theoretical properties are established and the performance of our procedures is evaluated and illustrated by numerical studies including a real-life data analysis.

Sign in / Sign up

Export Citation Format

Share Document