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/

Zendy Plus

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

Zendy Tools

Zendy Open

In linear regression, the least squares (LS) estimator has certain optimalityproperties if the errors are normally distributed. This assumption is oftenviolated in practice, partly caused by data outliers. Robust estimators cancope with this situation and thus they are widely used in practice. One exampleof robust estimators for regression are adaptive modified maximum likelihood(AMML) estimators (Donmez, 2010). However, they are not robust to $x$ outliers,so-called leverage points. In this study, we propose a new regression estimatorby employing an appropriate weighting scheme in the AMML estimation method. Theresulting estimator is called robust AMML (RAMML) since it is not only robustto y outliers but also to x outliers. A simulation study is carried out tocompare the performance of the RAMML estimator with some existing robustestimators such as MM, least trimmed squares (LTS) and S. The results show thatthe RAMML estimator is preferable in most settings according to the meansquared error (MSE) criterion. Two data sets taken from the literature are alsoanalyzed to show the implementation of the RAMML estimation methodology.

A Robust Adaptive Modified Maximum Likelihood Estimator for the Linear Regression Model