Kernel density estimator from ranked set samples

Johan Lim, Min Chen, Sangun Park, Xinlei Wang, Lynne Stokes

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


We study kernel density estimator from the ranked set samples (RSS). In the kernel density estimator, the selection of the bandwidth gives strong influence on the resulting estimate. In this article, we consider several different choices of the bandwidth and compare their asymptotic mean integrated square errors (MISE). We also propose a plug-in estimator of the bandwidth to minimize the asymptotic MISE. We numerically compare the MISE of the proposed kernel estimator (having the plug-in bandwidth estimator) to its simple random sampling counterpart. We further propose two estimators for a symmetric distribution, and show that they outperform in MISE all other estimators not considering symmetry. We finally apply the methods in this article to analyzing the tree height data from Platt et al. (1988) and Chen et al. (2003).

Original languageEnglish
Pages (from-to)2156-2168
Number of pages13
JournalCommunications in Statistics - Theory and Methods
Issue number10-12
Publication statusPublished - 2014 May 15

Bibliographical note

Funding Information:
This work was supported in part by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MEST) (2008-0061196).

All Science Journal Classification (ASJC) codes

  • Statistics and Probability


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