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

Robust inferential methods based on divergences measures have shown anappealing trade-off between efficiency and robustness in many differentstatistical models. In this paper, minimum density power divergence estimators(MDPDEs) for the scale and shape parameters of the log-logistic distributionare considered. The log-logistic is a versatile distribution modeling lifetimedata which is commonly adopted in survival analysis and reliability engineeringstudies when the hazard rate is initially increasing but then it decreasesafter some point. Further, it is shown that the classical estimators based onmaximum likelihood (MLE) are included as a particular case of the MDPDE family.Moreover, the corresponding influence function of the MDPDE is obtained, andits boundlessness is proved, thus leading to robust estimators. A simulationstudy is carried out to illustrate the slight loss in efficiency of MDPDE withrespect to MLE and, at besides, the considerable gain in robustness.

Robust parameter estimation of the log-logistic distribution based on density power divergence estimators