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

Motivated by studies in biological sciences to detect differentiallyexpressed genes, a semiparametric two-component mixture model with one knowncomponent is being studied in this paper. Assuming the density of the unknowncomponent to be log-concave, which contains a very broad family of densities,we develop a semiparametric maximum likelihood estimator and propose an EMalgorithm to compute it. Our new estimation method finds the mixing proportionsand the distribution of the unknown component simultaneously. We establish theidentifiability of the proposed semiparametric mixture model and prove theexistence and consistency of the proposed estimators. We further compare ourestimator with several existing estimators through simulation studies and applyour method to two real data sets from biological sciences and astronomy.

Maximum Likelihood Estimation of a Semiparametric Two-component Mixture Model using Log-concave Approximation