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Analysis of scientific military training data using zero-inflated and Hurdle regression
Journal of the Korean Data & Information Science Society 2017;28:1511-20
Published online November 30, 2017
© 2017 Korean Data & Information Science Society.

Jaeoh Kim1 · Sungwan Bang2 · Ojeong Kwon3

1Department of Statistics, Korea University
2Department of Mathematics, Korea Military Academy
3Department of Industrial and Systems Engineering, KAIST
Correspondence to: Ojeong Kwon
Invited Professor, Department of Industrial and Systems Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Korea. E-mail : kojjej@kaist.ac.kr
Received September 11, 2017; Revised October 20, 2017; Accepted November 2, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The purpose of this study is to analyze military combat training data to improve military operation and training methods and verify required military doctrine. We set the number of combat disabled enemies, which the individual combatants make using their weapons, as the response variable regarding offensive operations from scientific military training data of reinforced infantry battalion. Our response variable has more zero observations than would be allowed for by the traditional GLM such as Poisson regression. We used the zero-inflated regression and the hurdle regression for data analysis considering the over-dispersion and excessive zero observation problems. Our result can be utilized as an appropriate reference in order to verify a military doctrine for small units and analysis of various operational and tactical factors.
Keywords : Hurdle regression, over-dispersion, scientific combat training, zero-inflated regression