Minimum disparity estimation based on combined disparities: Asymptotic results

A. Mandal, S. K. Bhandari, A. Basu

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The maximum likelihood estimator (MLE) is asymptotically efficient for most parametric models under standard regularity conditions, but it has very poor robustness properties. On the other hand some of the minimum disparity estimators like the minimum Hellinger distance estimator (MHDE) have strong robustness features but their small sample efficiency at the model turns out to be very poor compared to the MLE. Methods based on the minimization of some combined disparities can substantially improve their small sample performances without affecting their robustness properties (Park et al., 1995). All studies involving the combined disparity have so far been empirical, and there are no results on the asymptotic properties of these estimators. In view of the usefulness of these procedures this is a major gap in theory, which we try to fill through the present work. Some illustrations of the performance of the estimators and the corresponding tests are also provided.

Original languageEnglish (US)
Pages (from-to)701-710
Number of pages10
JournalJournal of Statistical Planning and Inference
Issue number2
StatePublished - Feb 2011
Externally publishedYes


  • Asymptotic distribution
  • Combined disparity
  • Minimum disparity estimator


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