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

Model selection based on classical information criteria, such as BIC, isgenerally computationally demanding, but its properties are well studied. Onthe other hand, model selection based on parameter shrinkage by $\ell_1$-typepenalties is computationally efficient. In this paper we make an attempt tocombine their strengths, and propose a simple approach that penalizes thelikelihood with data-dependent $\ell_1$ penalties as in adaptive Lasso andexploits a fixed penalization parameter. Even for finite samples, its modelselection results approximately coincide with those based on informationcriteria; in particular, we show that in some special cases, this approach andthe corresponding information criterion produce exactly the same model. One canalso consider this approach as a way to directly determine the penalizationparameter in adaptive Lasso to achieve information criteria-like modelselection. As extensions, we apply this idea to complex models includingGaussian mixture model and mixture of factor analyzers, whose model selectionis traditionally difficult to do; by adopting suitable penalties, we providecontinuous approximators to the corresponding information criteria, which areeasy to optimize and enable efficient model selection.

Bridging Information Criteria and Parameter Shrinkage for Model Selection