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

Good robust estimators can be tuned to combine a high breakdown point and aspecified asymptotic efficiency at a central model. This happens in regressionwith MM- and tau-estimators among others. However, the finite-sample efficiencyof these estimators can be much lower than the asymptotic one. To overcome thisdrawback, an approach is proposed for parametric models, which is based on adistance between parameters. Given a robust estimator, the proposed one isobtained by maximizing the likelihood under the constraint that the distance isless than a given threshold. For the linear model with normal errors and usingthe MM estimator and the distance induced by the Kullback-Leibler divergence,simulations show that the proposed estimator attains a finite-sample efficiencyclose to one, while its maximum mean squared error under pointwise outliercontamination is smaller than that of the MM estimator. The same approach alsoshows good results in the estimation of multivariate location and scatter.

High finite-sample efficiency and robustness based on distance-constrained maximum likelihood