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

In prediction settings where data are collected over time, it is often ofinterest to understand both the importance of variables for predicting theresponse at each time point and the importance summarized over the time series.Building on recent advances in estimation and inference for variable importancemeasures, we define summaries of variable importance trajectories. Thesemeasures can be estimated and the same approaches for inference can be appliedregardless of the choice of the algorithm(s) used to estimate the predictionfunction. We propose a nonparametric efficient estimation and inferenceprocedure as well as a null hypothesis testing procedure that are valid evenwhen complex machine learning tools are used for prediction. Throughsimulations, we demonstrate that our proposed procedures have good operatingcharacteristics, and we illustrate their use by investigating the longitudinalimportance of risk factors for suicide attempt.

Inference on summaries of a model-agnostic longitudinal variable importance trajectory