Shrinkage estimation of proportion via logit penalty

Research output: Contribution to journalArticlepeer-review


By releasing the unbiasedness condition, we often obtain more accurate estimators due to the bias–variance trade-off. In this paper, we propose a class of shrinkage proportion estimators which show improved performance over the sample proportion. We provide the “optimal” amount of shrinkage. The advantage of the proposed estimators is given theoretically as well as explored empirically by simulation studies and real data analyses.

Original languageEnglish
Pages (from-to)2447-2453
Number of pages7
JournalCommunications in Statistics - Theory and Methods
Issue number5
Publication statusPublished - 2017 Mar 4
Externally publishedYes


  • Biased estimator
  • penalization
  • sample proportion
  • shrinkage proportion estimator

ASJC Scopus subject areas

  • Statistics and Probability


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