Nonparametric bootstrap inference for the targeted highly adaptive least absolute shrinkage and selection operator (LASSO) estimator

• Published in: The international journal of biostatistics Vol. 16; no. 2
• Main Authors: Cai, Weixin, van der Laan, Mark
• Format: Journal Article
• Language: English
• Published: Germany De Gruyter 10.08.2020; Walter de Gruyter GmbH
• Subjects: Asymptotically efficient estimator; Asymptotically linear estimator; Bootstrap method; Confidence intervals; Highly Adaptive LASSO (HAL); Nonparametric bootstrap; Nonparametric statistics; Sectional variation norm; Targeted Minimum Loss-based Estimation (TMLE); Targeted Minimum Loss-based Estimation (TMLE); Nonparametric bootstrap; Asymptotically linear estimator; Asymptotically efficient estimator; Sectional variation norm; Highly Adaptive LASSO (HAL)

The Highly-Adaptive least absolute shrinkage and selection operator (LASSO) Targeted Minimum Loss Estimator (HAL-TMLE) is an efficient plug-in estimator of a pathwise differentiable parameter in a statistical model that at minimal (and possibly only) assumes that the sectional variation norm of the...