Double-Semiparametric Method for Missing Covariates in Cox Regression Models

By Hua Yun Chen. 2002

56 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ceb1b2477e30e34f16877cad768c4a9131eda635

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Donglin Zeng
2. Danyu Lin
3. Eric B. Laber

In Statistics and Probability

1. Donglin Zeng
2. Danyu Lin
3. Hua Yun Chen
4. Eric B. Laber
5. Yi-Hau Chen
6. David J. Balding
7. Michael R. Kosorok
8. Jianqing Fan

Citations in the corpus

• Maximum likelihood estimation in semiparametric regression models with censored data (2007, background): “Chen and Little (1999) and Chen (2002) studied the NPMLEs for the proportional hazards model with missing covariates, whereas Scheike and Juul (2004) and Scheike and Martinussen (2004) considered the specific situations in which covariates are missing because of case–cohort or nested case–control…”
• Nonparametric and Semiparametric Models for Missing Covariates in Parametric Regression (2004, methodology): “This semiparametric modeling approach was proposed by Chen (2002).”