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

Improved EM strategies, based on the idea of efficient data augmentation(Meng and van Dyk 1997, 1998), are presented for ML estimation of mixtureproportions. The resulting algorithms inherit the simplicity, ease ofimplementation, and monotonic convergence properties of EM, but haveconsiderably improved speed. Because conventional EM tends to be slow whenthere exists a large overlap between the mixture components, we can improve thespeed without sacrificing the simplicity or stability, if we can reformulatethe problem so as to reduce the amount of overlap. We propose simple"squeezing" strategies for that purpose. Moreover, for high-dimensionalproblems, such as computing the nonparametric MLE of the distribution functionwith censored data, a natural and effective remedy for conventional EM is toadd exchange steps (based on improved EM) between adjacent mixture components,where the overlap is most severe. Theoretical considerations show that theresulting EM-type algorithms, when carefully implemented, are globallyconvergent. Simulated and real data examples show dramatic improvement in speedin realistic situations.

Improved EM for Mixture Proportions with Applications to Nonparametric ML Estimation for Censored Data