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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Nonparametric and machine learning methods are flexible methods for obtainingaccurate predictions. Nowadays, data sets with a large number of predictors andcomplex structures are fairly common. In the presence of item nonresponse,nonparametric and machine learning procedures may thus provide a usefulalternative to traditional imputation procedures for deriving a set of imputedvalues. In this paper, we conduct an extensive empirical investigation thatcompares a number of imputation procedures in terms of bias and efficiency in awide variety of settings, including high-dimensional data sets. The resultssuggest that a number of machine learning procedures perform very well in termsof bias and efficiency.

Imputation procedures in surveys using nonparametric and machine learning methods: an empirical comparison