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 many life science experiments or medical studies, subjects are repeatedlyobserved and measurements are collected in factorial designs with multivariatedata. The analysis of such multivariate data is typically based on multivariateanalysis of variance (MANOVA) or mixed models, requiring complete data, andcertain assumption on the underlying parametric distribution such as continuityor a specific covariance structure, e.g., compound symmetry. However, thesemethods are usually not applicable when discrete data or even orderedcategorical data are present. In such cases, nonparametric rank-based methodsthat do not require stringent distributional assumptions are the preferredchoice. However, in the multivariate case, most rank-based approaches have onlybeen developed for complete observations. It is the aim of this work is todevelop asymptotic correct procedures that are capable of handling missingvalues, allowing for singular covariance matrices and are applicable forordinal or ordered categorical data. This is achieved by applying a wildbootstrap procedure in combination with quadratic form-type test statistics.Beyond proving their asymptotic correctness, extensive simulation studiesvalidate their applicability for small samples. Finally, two real data examplesare analyzed.

Incompletely observed nonparametric factorial designs with repeated measurements: A wild bootstrap approach