Multiplicity Adjustments for Differences in Proportion Parameters in Multiple-Sample Misclassified Binary Data
Generally, following an omnibus (overall equality) test, multiple pairwise comparison (MPC) tests are typically conducted as the second step in a sequential testing procedure to identify which specific pairs (e.g., proportions) exhibit significant differences. In this manuscript, we develop maximum...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2813-2203/4/2/15 |
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| author | Dewi Rahardja |
| author_facet | Dewi Rahardja |
| author_sort | Dewi Rahardja |
| collection | DOAJ |
| description | Generally, following an omnibus (overall equality) test, multiple pairwise comparison (MPC) tests are typically conducted as the second step in a sequential testing procedure to identify which specific pairs (e.g., proportions) exhibit significant differences. In this manuscript, we develop maximum likelihood estimation (MLE) methods to construct three different types of confidence intervals (CIs) for multiple pairwise differences in proportions, specifically in contexts where both types of misclassifications (i.e., over-reporting and under-reporting) exist in multiple-sample binomial data. Our closed-form algorithm is straightforward to implement. Consequently, when dealing with multiple sample proportions, we can readily apply MPC adjustment procedures—such as Bonferroni, Šidák, and Dunn—to address the issue of multiplicity. This manuscript advances the existing literature by extending from scenarios with only one type of misclassification to those involving both. Furthermore, we demonstrate our methods using a real-world data example. |
| format | Article |
| id | doaj-art-9bc50bdc92c94f5989a848b38bc45aae |
| institution | Kabale University |
| issn | 2813-2203 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Analytics |
| spelling | doaj-art-9bc50bdc92c94f5989a848b38bc45aae2025-08-20T03:24:29ZengMDPI AGAnalytics2813-22032025-05-01421510.3390/analytics4020015Multiplicity Adjustments for Differences in Proportion Parameters in Multiple-Sample Misclassified Binary DataDewi Rahardja0U.S. Department of Defense, Fort Meade, MD 20755, USAGenerally, following an omnibus (overall equality) test, multiple pairwise comparison (MPC) tests are typically conducted as the second step in a sequential testing procedure to identify which specific pairs (e.g., proportions) exhibit significant differences. In this manuscript, we develop maximum likelihood estimation (MLE) methods to construct three different types of confidence intervals (CIs) for multiple pairwise differences in proportions, specifically in contexts where both types of misclassifications (i.e., over-reporting and under-reporting) exist in multiple-sample binomial data. Our closed-form algorithm is straightforward to implement. Consequently, when dealing with multiple sample proportions, we can readily apply MPC adjustment procedures—such as Bonferroni, Šidák, and Dunn—to address the issue of multiplicity. This manuscript advances the existing literature by extending from scenarios with only one type of misclassification to those involving both. Furthermore, we demonstrate our methods using a real-world data example.https://www.mdpi.com/2813-2203/4/2/15multiple pairwise comparisons (MPCs)binomial datadouble samplingmisclassificationmultiple samplesproportion differences |
| spellingShingle | Dewi Rahardja Multiplicity Adjustments for Differences in Proportion Parameters in Multiple-Sample Misclassified Binary Data Analytics multiple pairwise comparisons (MPCs) binomial data double sampling misclassification multiple samples proportion differences |
| title | Multiplicity Adjustments for Differences in Proportion Parameters in Multiple-Sample Misclassified Binary Data |
| title_full | Multiplicity Adjustments for Differences in Proportion Parameters in Multiple-Sample Misclassified Binary Data |
| title_fullStr | Multiplicity Adjustments for Differences in Proportion Parameters in Multiple-Sample Misclassified Binary Data |
| title_full_unstemmed | Multiplicity Adjustments for Differences in Proportion Parameters in Multiple-Sample Misclassified Binary Data |
| title_short | Multiplicity Adjustments for Differences in Proportion Parameters in Multiple-Sample Misclassified Binary Data |
| title_sort | multiplicity adjustments for differences in proportion parameters in multiple sample misclassified binary data |
| topic | multiple pairwise comparisons (MPCs) binomial data double sampling misclassification multiple samples proportion differences |
| url | https://www.mdpi.com/2813-2203/4/2/15 |
| work_keys_str_mv | AT dewirahardja multiplicityadjustmentsfordifferencesinproportionparametersinmultiplesamplemisclassifiedbinarydata |