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

We here provide a distribution-free approach to the random factor analysismodel. We show that it leads to the same estimating equations as for theclassical ML estimates under normality, but more easily derived, and valid alsoin the case of more variables than observations ($p>n$). For this case we alsoadvocate a simple iteration method. In an illustration with $p=2000$ and $n=22$it was seen to lead to convergence after just a few iterations. We show thatthere is no reason to expect Heywood cases to appear, and that the factorscores will typically be precisely estimated/predicted as soon as $p$ is large.We state as a general conjecture that the nice behaviour is not despite $p>n$,but because $p>n$.

Distribution-free factor analysis - Estimation theory and applicability to high-dimensional data