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Alternative Modeling Strategies for Characterizing Radio Performance
Author(s) -
Zessin Cynthia G.,
Oelrich Michael H.,
Hutto Gregory T.,
Romeo Amber,
Rios Armando J.,
Simpson James R.
Publication year - 2017
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1995
Subject(s) - logistic regression , poisson regression , ordinal regression , computer science , ordinal data , regression analysis , statistics , ordered logit , goodness of fit , ordinary least squares , field (mathematics) , variables , regression diagnostic , econometrics , mathematics , machine learning , polynomial regression , population , demography , sociology , pure mathematics
Historically, the application of logistic and Poisson regression has been focused in the social science and medical fields where the response variable typically has only a few possible outcomes. These techniques are not commonly applied to characterize military operations even though response variables that measure success or failure are commonly encountered in this field. This paper explores the application of ordinal logistic and Poisson regression as alternatives to ordinary least squares estimation for modeling operational performance in a military testing environment. The operational test planners chose a nested face‐centered experimental design, which was executed to collect test data. Three modeling techniques were employed in the analysis: multiple linear regression, ordinal logistic regression, and Poisson regression. The purpose of the study was to determine which regression technique best fits the test data. Cross validation and model goodness comparison were accomplished by assessing that the model fits for each model type in combination with a comparison of significant main effects and interactions. Finally, contrasts are provided relative to the ease of implementing each technique. Copyright © 2016 John Wiley & Sons, Ltd.