Multiple Comparison Procedures for the Differences of Proportion Parameters in Over-Reported Multiple-Sample Binomial Data
In sequential tests, typically a (pairwise) multiple comparison procedure (MCP) is performed after an omnibus test (an overall equality test). In general, when an omnibus test (e.g., overall equality of multiple proportions test) is rejected, then we further conduct a (pairwise) multiple comparisons...
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doaj-8ee8240d82694da9805a747e109421332020-11-25T02:10:34ZengMDPI AGStats2571-905X2020-03-0131566710.3390/stats3010006stats3010006Multiple Comparison Procedures for the Differences of Proportion Parameters in Over-Reported Multiple-Sample Binomial DataDewi Rahardja0U.S. Department of Defense, Fort Meade, MD 20755, USAIn sequential tests, typically a (pairwise) multiple comparison procedure (MCP) is performed after an omnibus test (an overall equality test). In general, when an omnibus test (e.g., overall equality of multiple proportions test) is rejected, then we further conduct a (pairwise) multiple comparisons or MCPs to determine which (e.g., proportions) pairs the significant differences came from. In this article, via likelihood-based approaches, we acquire three confidence intervals (CIs) for comparing each pairwise proportion difference in the presence of over-reported binomial data. Our closed-form algorithm is easy to implement. As a result, for multiple-sample proportions differences, we can easily apply MCP adjustment methods (e.g., Bonferroni, Šidák, and Dunn) to address the multiplicity issue, unlike previous literatures. We illustrate our procedures to a real data example.https://www.mdpi.com/2571-905X/3/1/6multiple comparison procedure (mcp)pairwise comparisonsbinary datadouble samplingmisclassificationmultiple-sampleproportions difference |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dewi Rahardja |
spellingShingle |
Dewi Rahardja Multiple Comparison Procedures for the Differences of Proportion Parameters in Over-Reported Multiple-Sample Binomial Data Stats multiple comparison procedure (mcp) pairwise comparisons binary data double sampling misclassification multiple-sample proportions difference |
author_facet |
Dewi Rahardja |
author_sort |
Dewi Rahardja |
title |
Multiple Comparison Procedures for the Differences of Proportion Parameters in Over-Reported Multiple-Sample Binomial Data |
title_short |
Multiple Comparison Procedures for the Differences of Proportion Parameters in Over-Reported Multiple-Sample Binomial Data |
title_full |
Multiple Comparison Procedures for the Differences of Proportion Parameters in Over-Reported Multiple-Sample Binomial Data |
title_fullStr |
Multiple Comparison Procedures for the Differences of Proportion Parameters in Over-Reported Multiple-Sample Binomial Data |
title_full_unstemmed |
Multiple Comparison Procedures for the Differences of Proportion Parameters in Over-Reported Multiple-Sample Binomial Data |
title_sort |
multiple comparison procedures for the differences of proportion parameters in over-reported multiple-sample binomial data |
publisher |
MDPI AG |
series |
Stats |
issn |
2571-905X |
publishDate |
2020-03-01 |
description |
In sequential tests, typically a (pairwise) multiple comparison procedure (MCP) is performed after an omnibus test (an overall equality test). In general, when an omnibus test (e.g., overall equality of multiple proportions test) is rejected, then we further conduct a (pairwise) multiple comparisons or MCPs to determine which (e.g., proportions) pairs the significant differences came from. In this article, via likelihood-based approaches, we acquire three confidence intervals (CIs) for comparing each pairwise proportion difference in the presence of over-reported binomial data. Our closed-form algorithm is easy to implement. As a result, for multiple-sample proportions differences, we can easily apply MCP adjustment methods (e.g., Bonferroni, Šidák, and Dunn) to address the multiplicity issue, unlike previous literatures. We illustrate our procedures to a real data example. |
topic |
multiple comparison procedure (mcp) pairwise comparisons binary data double sampling misclassification multiple-sample proportions difference |
url |
https://www.mdpi.com/2571-905X/3/1/6 |
work_keys_str_mv |
AT dewirahardja multiplecomparisonproceduresforthedifferencesofproportionparametersinoverreportedmultiplesamplebinomialdata |
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1724918889759899648 |