Simultaneous confidence intervals for contrasts of quantiles

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Original languageEnglish
Pages (from-to)7-19
Number of pages13
JournalBiometrical Journal
Volume64
Issue number1
Early online date9 Sept 2021
Publication statusPublished - 11 Jan 2022

Abstract

Skewed distributions and inferences concerning quantiles are common in the health and social science researches. And most standard simultaneous inference procedures require the normality assumption. For example, few methods exist for comparing the medians of independent samples or quantiles of several distributions in general. To our knowledge, there is no easy-to-use method for constructing simultaneous confidence intervals for multiple contrasts of quantiles in a one-way layout. In this paper, we develop an asymptotic method for constructing such intervals both for differences and ratios of quantiles and extend the idea to that of right-censored time-to-event data in survival analysis. Small-sample performance of the proposed method and a bootstrap method were assessed in terms of coverage probabilities and average widths of the simultaneous confidence intervals. Good coverage probabilities were observed for most of the distributions considered in our simulations. The proposed methods have been implemented in an R package and are used to analyze two motivating datasets.

Keywords

    asymptotic, confidence intervals, kernel density, multiple contrasts, quantiles

ASJC Scopus subject areas

Cite this

Simultaneous confidence intervals for contrasts of quantiles. / Segbehoe, Lawrence S.; Schaarschmidt, Frank; Djira, Gemechis D.
In: Biometrical Journal, Vol. 64, No. 1, 11.01.2022, p. 7-19.

Research output: Contribution to journalArticleResearchpeer review

Segbehoe LS, Schaarschmidt F, Djira GD. Simultaneous confidence intervals for contrasts of quantiles. Biometrical Journal. 2022 Jan 11;64(1):7-19. Epub 2021 Sept 9. doi: 10.1002/bimj.202000077
Segbehoe, Lawrence S. ; Schaarschmidt, Frank ; Djira, Gemechis D. / Simultaneous confidence intervals for contrasts of quantiles. In: Biometrical Journal. 2022 ; Vol. 64, No. 1. pp. 7-19.
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