A new quantile estimator with weights based on a subsampling approach

Navruz G., Özdemir A. F.

BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY, vol.73, no.3, pp.506-521, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 73 Issue: 3
  • Publication Date: 2020
  • Doi Number: 10.1111/bmsp.12198
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, IBZ Online, EMBASE, MEDLINE, Psycinfo, Sociological abstracts, zbMATH
  • Page Numbers: pp.506-521
  • Keywords: NO quantile estimator, asymptotic properties, percentile bootstrap, two independent groups, 2 INDEPENDENT GROUPS
  • Dokuz Eylül University Affiliated: Yes


Quantiles are widely used in both theoretical and applied statistics, and it is important to be able to deploy appropriate quantile estimators. To improve performance in the lower and upper quantiles, especially with small sample sizes, a new quantile estimator is introduced which is a weighted average of all order statistics. The new estimator, denoted NO, has desirable asymptotic properties. Moreover, it offers practical advantages over four estimators in terms of efficiency in most experimental settings. The Harrell-Davis quantile estimator, the default quantile estimator of the R programming language, the Sfakianakis-Verginis SV2 quantile estimator and a kernel quantile estimator. The NO quantile estimator is also utilized in comparing two independent groups with a percentile bootstrap method and, as expected, it is more successful than other estimators in controlling Type I error rates.