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

Missing data are often dealt with multiple imputation. A crucial part of themultiple imputation process is selecting sensible models to generate plausiblevalues for incomplete data. A method based on posterior predictive checking isproposed to diagnose imputation models based on posterior predictive checking.To assess the congeniality of imputation models, the proposed diagnostic methodcompares the observed data with their replicates generated under correspondingposterior predictive distributions. If the imputation model is congenial withthe substantive model, the observed data are expected to be located in thecentre of corresponding predictive posterior distributions. Simulation andapplication are designed to investigate the proposed diagnostic method forparametric and semi-parametric imputation approaches, continuous and discreteincomplete variables, univariate and multivariate missingness patterns. Theresults show the validity of the proposed diagnostic method.

Graphical and numerical diagnostic tools to assess multiple imputation models by posterior predictive checking