Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 7-19 |
| Seitenumfang | 13 |
| Fachzeitschrift | Biometrical Journal |
| Jahrgang | 64 |
| Ausgabenummer | 1 |
| Frühes Online-Datum | 9 Sept. 2021 |
| Publikationsstatus | Veröffentlicht - 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.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Statistik und Wahrscheinlichkeit
- Entscheidungswissenschaften (insg.)
- Statistik, Wahrscheinlichkeit und Ungewissheit
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
in: Biometrical Journal, Jahrgang 64, Nr. 1, 11.01.2022, S. 7-19.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Simultaneous confidence intervals for contrasts of quantiles
AU - Segbehoe, Lawrence S.
AU - Schaarschmidt, Frank
AU - Djira, Gemechis D.
PY - 2022/1/11
Y1 - 2022/1/11
N2 - 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.
AB - 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.
KW - asymptotic
KW - confidence intervals
KW - kernel density
KW - multiple contrasts
KW - quantiles
UR - http://www.scopus.com/inward/record.url?scp=85114488317&partnerID=8YFLogxK
U2 - 10.1002/bimj.202000077
DO - 10.1002/bimj.202000077
M3 - Article
AN - SCOPUS:85114488317
VL - 64
SP - 7
EP - 19
JO - Biometrical Journal
JF - Biometrical Journal
SN - 0323-3847
IS - 1
ER -