Article
Publicly Available
Estimating a Survival Distribution with Current Status Data and High-dimensional Covariates
-
Aad van der Vaart
and Mark J. van der Laan
Published/Copyright:
October 10, 2006
Published Online: 2006-10-10
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Articles in the same Issue
- Article
- A Regression Model for Dependent Gap Times
- Statistical Inference for Variable Importance
- Statistical Classification of Abnormal Blood Profiles in Athletes
- Relationship between Derivatives of the Observed and Full Loglikelihoods and Application to Newton-Raphson Algorithm
- The Two Sample Problem for Multiple Categorical Variables
- Application of a Variable Importance Measure Method
- Choice of Monitoring Mechanism for Optimal Nonparametric Functional Estimation for Binary Data
- Approximate Power and Sample Size Calculations with the Benjamini-Hochberg Method
- Estimating a Survival Distribution with Current Status Data and High-dimensional Covariates
- An Improved Akaike Information Criterion for Generalized Log-Gamma Regression Models
- Targeted Maximum Likelihood Learning
- Modeling the Effect of a Preventive Intervention on the Natural History of Cancer: Application to the Prostate Cancer Prevention Trial
Keywords for this article
semiparametric model;
curse of dimensionality;
isotonic estimation;
censoring;
coarsening-at-random
Articles in the same Issue
- Article
- A Regression Model for Dependent Gap Times
- Statistical Inference for Variable Importance
- Statistical Classification of Abnormal Blood Profiles in Athletes
- Relationship between Derivatives of the Observed and Full Loglikelihoods and Application to Newton-Raphson Algorithm
- The Two Sample Problem for Multiple Categorical Variables
- Application of a Variable Importance Measure Method
- Choice of Monitoring Mechanism for Optimal Nonparametric Functional Estimation for Binary Data
- Approximate Power and Sample Size Calculations with the Benjamini-Hochberg Method
- Estimating a Survival Distribution with Current Status Data and High-dimensional Covariates
- An Improved Akaike Information Criterion for Generalized Log-Gamma Regression Models
- Targeted Maximum Likelihood Learning
- Modeling the Effect of a Preventive Intervention on the Natural History of Cancer: Application to the Prostate Cancer Prevention Trial