Robustification of the MLE without Loss of Efficiency

Biman Chakraborty, Sahadeb Sarkar, Ayanendranath Basu

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract

A robust procedure, which produces the maximum likelihood estimator when the data are in conformity with the parametric model, and generates the outlier deleted maximum likelihood estimator under the presence of extreme outliers, has obvious intuitive appeal to the practising scientist. None of the currently available robust estimators achieves this automatically. Here we propose a density-based divergence belonging to the family of disparities ([7]) where the corresponding weighted likelihood estimator ([10], [11]) exhibits this desirable behavior for proper choices of tuning parameters. Some properties of the corresponding estimation procedure are discussed and illustrated through examples.

Original languageEnglish
Title of host publicationModern Mathematical Tools and Techniques in Capturing Complexity
EditorsLeandro Pardo, Narayanswami Balakrishnan, Maria Gil
Place of PublicationBerlin
PublisherSpringer
Pages423-436
Number of pages14
Edition1
ISBN (Electronic)9783642208539
ISBN (Print)9783642208522, 9783642268359
DOIs
Publication statusPublished - 26 May 2011

Publication series

NameUnderstanding Complex Systems
PublisherSpringer
ISSN (Print)1860-0832
ISSN (Electronic)1860-0840

Keywords

  • Hellinger distance
  • outlier deleted maximum likelihood estimator,
  • residual adjustment function
  • Hellinger distance, outlier deleted maximum likelihood estimatorweighted likelihood estimation.

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