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

Dewi Rahardja
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Dewi Rahardja: Department of Defense, Fort Meade, MD 20755, USA

Stats, 2022, vol. 5, issue 2, 1-14

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.

Keywords: omnibus test; two-stage sequential test; multiple binomial proportions; over-reported; under-reported data; miscategorization; doubly sampled framework (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
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