Targeted Minimum Loss Based Estimator that Outperforms a given Estimator
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DOI: 10.1515/1557-4679.1332
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References listed on IDEAS
- Weihua Cao & Anastasios A. Tsiatis & Marie Davidian, 2009. "Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data," Biometrika, Biometrika Trust, vol. 96(3), pages 723-734.
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- Iván Díaz & Elizabeth Colantuoni & Daniel F. Hanley & Michael Rosenblum, 2019. "Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 439-468, July.
- Nicholas Williams & Michael Rosenblum & Iván Díaz, 2022. "Optimising precision and power by machine learning in randomised trials with ordinal and time‐to‐event outcomes with an application to COVID‐19," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2156-2178, October.
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Keywords
Asymptotic linearity of an estimator; causal effect; efficient influence curve; empirical efficiency maximization; confounding; G-computation formula; influence curve; loss function; nonparametric structural equation model; positivity assumption; randomization assumption; randomized trial; semiparametric statistical model; targeted maximum likelihood estimation; targeted minimum loss based estimation;All these keywords.
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