Omnibus Tests for Multiple Binomial Proportions via Doubly Sampled Framework with Under-Reported Data

Author:

Rahardja Dewi

Abstract

Previously, Rahardja (2020) paper (in the first reference list) developed a (pairwise) multiple comparison procedure (MCP) to determine which (proportions) pairs of Multiple Binomial Proportions (with under-reported data), the significant differences came from. Generally, such an MCP test (developed by Rahardja, 2020) is the second part of a two-stage sequential test. In this paper, we derived two omnibus tests (i.e., the overall equality of multiple proportions test) as the first part of the above two-stage sequential test (with under-reported data), in general. Using two likelihood-based approaches, we acquire two Wald-type (Omnibus) tests to compare Multiple Binomial Proportions (in the presence of under-reported data). Our closed-form algorithm is easy to implement and not computationally burdensome. We applied our algorithm to a vehicle-accident data example.

Publisher

MDPI AG

Reference22 articles.

1. Multiple Comparison Procedures for the Differences of Proportion Parameters in Over-Reported Multiple-Sample Binomial Data

2. Misclassification in 2 X 2 Tables

3. The effects of misclassification on the bias in the difference between two proportions and the relative odds in the fourfold table;Goldberg;J. Am. Stat. Assoc.,1975

4. A Double Sampling Scheme for Estimating from Binomial Data with Misclassifications

5. Repeated audit controls

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