English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

When estimating target parameters in nonparametric models with nuisanceparameters, substituting the unknown nuisances with nonparametric estimatorscan introduce ``plug-in bias.'' Traditional methods addressing this suboptimalbias-variance trade-off rely on the \emph{influence function} (IF) of thetarget parameter. When estimating multiple target parameters, these methodsrequire debiasing the nuisance parameter multiple times using the correspondingIFs, which poses analytical and computational challenges. In this work, weleverage the \emph{targeted maximum likelihood estimation} (TMLE) framework topropose a novel method named \emph{kernel debiased plug-in estimation} (KDPE).KDPE refines an initial estimate through regularized likelihood maximizationsteps, employing a nonparametric model based on \emph{reproducing kernelHilbert spaces}. We show that KDPE: (i) simultaneously debiases \emph{all}pathwise differentiable target parameters that satisfy our regularityconditions, (ii) does not require the IF for implementation, and (iii) remainscomputationally tractable. We numerically illustrate the use of KDPE andvalidate our theoretical results.

Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters