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/

Global: Zendy Plus annual

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

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Suppose that we wish to estimate a finite-dimensional summary of one or morefunction-valued features of an underlying data-generating mechanism under anonparametric model. One approach to estimation is by plugging in flexibleestimates of these features. Unfortunately, in general, such estimators may notbe asymptotically efficient, which often makes these estimators difficult touse as a basis for inference. Though there are several existing methods toconstruct asymptotically efficient plug-in estimators, each such method eithercan only be derived using knowledge of efficiency theory or is only valid understringent smoothness assumptions. Among existing methods, sieve estimatorsstand out as particularly convenient because efficiency theory is not requiredin their construction, their tuning parameters can be selected data adaptively,and they are universal in the sense that the same fits lead to efficientplug-in estimators for a rich class of estimands. Inspired by these desirableproperties, we propose two novel universal approaches for estimatingfunction-valued features that can be analyzed using sieve estimation theory.Compared to traditional sieve estimators, these approaches are valid under moregeneral conditions on the smoothness of the function-valued features byutilizing flexible estimates that can be obtained, for example, using machinelearning.

Universal sieve-based strategies for efficient estimation using machine learning tools