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Nonparametric bootstrap inference for the targeted highly adaptive least absolute shrinkage and selection operator

Weixin Cai1, Mark van der Laan1

  • 1Division of Biostatistics, University of California, Berkeley, USA.

The International Journal of Biostatistics
|August 11, 2020
PubMed
Summary

The Highly-Adaptive LASSO Targeted Minimum Loss Estimator (HAL-TMLE) uses nonparametric bootstrap for consistent estimation. A novel method optimizes confidence intervals by selecting the sectional variation norm for better finite sample coverage.

Keywords:
Asymptotically efficient estimatorAsymptotically linear estimatorHighly Adaptive LASSO (HAL)Nonparametric bootstrapSectional variation normTargeted Minimum Loss-based Estimation (TMLE)

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