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New Targeted Highly Adaptive Lasso method improves statistical estimation

Researchers have introduced a new statistical method called Targeted Highly Adaptive Lasso (Targeted HAL) for estimating non-pathwise differentiable functional parameters, such as dose-response curves. This method utilizes spline basis functions and a LASSO step to approximate the target function, aiming for improved accuracy and data-adaptive inference. Simulations indicate that Targeted HAL outperforms existing HAL plug-in estimators in terms of bias and mean squared error, offering a flexible approach without requiring parametric assumptions. AI

IMPACT Introduces a novel statistical technique that could enhance machine learning model accuracy and data-driven inference.

RANK_REASON The cluster contains two identical arXiv preprints detailing a new statistical methodology.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New Targeted Highly Adaptive Lasso method improves statistical estimation

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Vanessa Rodriguez, Karla Diaz-Ordaz, Brieuc Lehmann, Mark J. van der Laan ·

    Targeted Highly Adaptive Lasso Minimum Loss Estimation of Target Functions

    arXiv:2607.03824v1 Announce Type: cross Abstract: We propose a Targeted Highly Adaptive Lasso for estimation of non-pathwise differentiable functional parameters such as the dose-response curve (DRC) for continuous exposure. We assume the target function lies in the $k$-th order …

  2. arXiv stat.ML TIER_1 English(EN) · Mark J. van der Laan ·

    Targeted Highly Adaptive Lasso Minimum Loss Estimation of Target Functions

    We propose a Targeted Highly Adaptive Lasso for estimation of non-pathwise differentiable functional parameters such as the dose-response curve (DRC) for continuous exposure. We assume the target function lies in the $k$-th order smoothness class used to define the $k$-th order H…