Simultaneous confidence intervals for ranks using the partitioning principle
Electronic Journal of Statistics, vol. 15
Abstract
We consider the problem of constructing simultaneous confidence intervals (CIs) for the ranks of n means based on their estimates together with the (known) standard errors of those estimates. We present a generic method based on the partitioning principle in which the parameter space is partitioned into disjoint subsets and then each one of them is tested at level α. The resulting CIs have then a simultaneous coverage of 1−α. We show that any procedure which produces simultaneous CIs for ranks can be written as a partitioning procedure. We present a first example where we test the partitions using the likelihood ratio (LR) test. Then, in a second example we show that a recently proposed method for simultaneous CIs for ranks using Tukey’s honest significant difference test has an equivalent procedure based on the partitioning principle. By embedding these two methods inside our generic partitioning procedure, we obtain improved variants. We illustrate the performance of these methods through simulations and real data analysis on hotel ratings. While the novel method that uses the LR test and its variant produce shorter CIs when the number of means is small, the Tukey-based method and its variant produce shorter CIs when the number of means is high.
Authors 4
-
Diaa Al Mohamad Aachen
Leiden University Medical Center
Affiliation as printed
Leiden University Medical Center, Einthovenweg 20. 2333 ZC Leiden, The Nethlerlands
-
Erik W. van Zwet Aachen
Leiden University Medical Center
Affiliation as printed
Leiden University Medical Center, Einthovenweg 20. 2333 ZC Leiden, The Nethlerlands
-
Affiliation as printed
University of Milano-Bicocca, 1 Piazza dell’Ateneo Nuovo. 20126 Milano, Italy
-
Jelle J. Goeman Aachen
Leiden University Medical Center
Affiliation as printed
Leiden University Medical Center, Einthovenweg 20. 2333 ZC Leiden, The Nethlerlands
Cited by 2 stored of 2
2 results
No patents citing this paper on Lens.org (checked 2026-10-11).