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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Missing data present challenges in data analysis. Naive analyses such ascomplete-case and available-case analysis may introduce bias and loss ofefficiency, and produce unreliable results. Multiple imputation (MI) is one ofthe most widely used methods for handling missing data which can be partlyattributed to its ease of use. However, existing MI methods implemented in moststatistical software are not applicable to or do not perform well inhigh-dimensional settings where the number of predictors is large relative tothe sample size. To remedy this issue, we develop an MI approach that usesdimension reduction techniques. Specifically, in constructing imputation modelsin the presence of high-dimensional data our approach uses sure independentscreening followed by either sparse principal component analysis (sPCA) orsufficient dimension reduction (SDR) techniques. Our simulation studies,conducted for high-dimensional data, demonstrate that using SIS followed bysPCA to perform MI achieves better performance than the other imputationmethods including several existing imputation approaches. We apply our approachto analysis of gene expression data from a prostate cancer study.

Multiple imputation using dimension reduction techniques for high-dimensional data