Targeted Maximum Likelihood Learning

Mark J. van der Laan; Daniel Rubin

doi:10.2202/1557-4679.1043

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Targeted Maximum Likelihood Learning

Mark J. van der Laan and Daniel Rubin

Published/Copyright: December 28, 2006

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From the journal The International Journal of Biostatistics Volume 2 Issue 1

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MLA
APA
Harvard
Chicago
Vancouver

van der Laan, Mark J. and Rubin, Daniel. "Targeted Maximum Likelihood Learning" The International Journal of Biostatistics, vol. 2, no. 1. https://doi.org/10.2202/1557-4679.1043

van der Laan, M. & Rubin, D. (). Targeted Maximum Likelihood Learning. The International Journal of Biostatistics, 2(1). https://doi.org/10.2202/1557-4679.1043

van der Laan, M. and Rubin, D. () Targeted Maximum Likelihood Learning. The International Journal of Biostatistics, Vol. 2 (Issue 1). https://doi.org/10.2202/1557-4679.1043

van der Laan, Mark J. and Rubin, Daniel. "Targeted Maximum Likelihood Learning" The International Journal of Biostatistics 2, no. 1 (). https://doi.org/10.2202/1557-4679.1043

van der Laan M, Rubin D. Targeted Maximum Likelihood Learning. The International Journal of Biostatistics. ;2(1). https://doi.org/10.2202/1557-4679.1043

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Published Online: 2006-12-28

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https://doi.org/10.2202/1557-4679.1043

Keywords for this article

causal effect; cross-validation; efficient influence curve; estimating function; locally efficient estimation; loss function; maximum likelihood estimation; sieve; targeted maximum likelihood estimation; variable importance

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