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

Factor Analysis is a popular method for modeling dependence in multivariatedata. However, determining the number of factors and obtaining a sparseorientation of the loadings are still major challenges. In this paper, wepropose a decision-theoretic approach that brings to light the relation betweena sparse representation of the loadings and factor dimension. This relation isdone through a summary from information contained in the multivariateposterior. To construct such summary, we introduce a three-step approach. Inthe first step, the model is fitted with a conservative factor dimension. Inthe second step, a series of sparse point-estimates, with a decreasing numberof factors, is obtained by minimizing an expected predictive loss function. Instep three, the degradation in utility in relation to the sparse loadings andfactor dimensions is displayed in the posterior summary. The findings areillustrated with applications in classical data from the Factor Analysisliterature. We used different prior choices and factor dimensions todemonstrate the flexibility of the proposed method.

Decoupling Shrinkage and Selection in Gaussian Linear Factor Analysis