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

We address the practical construction of asymptotic confidence intervals forsmooth (i.e., path-wise differentiable), real-valued statistical parameters bytargeted learning from independent and identically distributed data in contextswhere sample size is so large that it poses computational challenges. Weobserve some summary measure of all data and select a sub-sample from thecomplete data set by Poisson rejective sampling with unequal inclusionprobabilities based on the summary measures. Targeted learning is carried outfrom the easier to handle sub-sample. We derive a central limit theorem for thetargeted minimum loss estimator (TMLE) which enables the construction of theconfidence intervals. The inclusion probabilities can be optimized to reducethe asymptotic variance of the TMLE. We illustrate the procedure with twoexamples where the parameters of interest are variable importance measures ofan exposure (binary or continuous) on an outcome. We also conduct a simulationstudy and comment on its results. keywords: semiparametric inference; surveysampling; targeted minimum loss estimation (TMLE)

Practical targeted learning from large data sets by survey sampling