A new quantile estimator with weights based on a subsampling approach

Navruz G., Özdemir A. F.

BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY, cilt.73, sa.3, ss.506-521, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 73 Sayı: 3
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1111/bmsp.12198
  • Dergi Adı: BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, IBZ Online, EMBASE, MEDLINE, Psycinfo, Sociological abstracts, zbMATH
  • Sayfa Sayıları: ss.506-521
  • Anahtar Kelimeler: NO quantile estimator, asymptotic properties, percentile bootstrap, two independent groups, 2 INDEPENDENT GROUPS
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

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.