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Inference for Under-Dispersed Data: Assessing the Performance of an Airborne Spacing AlgorithmPoisson regression is a commonly used tool for analyzing rate data; however, the assumption that the mean and variance of a process are equal rarely holds true in practice. When this assumption is violated, a quasi-Poisson distribution can be used to account for the existing over- or under-dispersion. This article presents an analysis of a study conducted by NASA to assess the performance of a new airborne spacing algorithm. A deterministic computer simulation was conducted to examine the algorithm in various conditions designed to simulate real-life scenarios, and two measures of algorithm performance were modeled using both continuous and categorical factors. Due to the presence of under-dispersion, tests for significance of main effects and two-factor interactions required bias adjustment. This article presents a comparison of tests of effects for the Poisson and quasi-Poisson models, details of fitting these models using common statistical software packages, and calculation of dispersion tests.
Document ID
20190027465
Document Type
Accepted Manuscript (Version with final changes)
Authors
Wilson, Sara R.
(NASA Langley Research Center Hampton, VA, United States)
Leonard, Robert D.
(Miami Univ. Coral Gables, FL, United States)
Edwards, David J.
(Virginia Commonwealth Univ. Richmond, VA, United States)
Swieringa, Kurt A.
(NASA Langley Research Center Hampton, VA, United States)
Underwood, Matthew C.
(NASA Langley Research Center Hampton, VA, United States)
Date Acquired
July 18, 2019
Publication Date
October 18, 2018
Publication Information
Publication: Quality Engineering
Volume: 30
Issue: 4
ISSN: 0898-2112
Subject Category
Numerical Analysis
Report/Patent Number
NF1676L-23831
Funding Number(s)
WBS: 330693.04.10.07.07
PROJECT: ARMD_330693
Distribution Limits
Public
Copyright
Public Use Permitted.

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