Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Ordinal Logistic Regression as a Tool To Estimate the Risk of Escalating Outcomes. An Application to Vehicle Crash Data

Charalambos Gnardellis
* ORCID logo ,
Venetia Notara
ORCID logo ,
Georgia Tzamalouka
,
Maria Papadakaki
ORCID logo ,
Joannes Chliaoutakis
ORCID logo
Version 1 : Received: 26 April 2023 / Approved: 27 April 2023 / Online: 27 April 2023 (04:02:49 CEST)

How to cite: Gnardellis, C.; Notara, V.; Tzamalouka, G.; Papadakaki, M.; Chliaoutakis, J. Ordinal Logistic Regression as a Tool To Estimate the Risk of Escalating Outcomes. An Application to Vehicle Crash Data. Preprints 2023, 2023041023. https://doi.org/10.20944/preprints202304.1023.v1 Gnardellis, C.; Notara, V.; Tzamalouka, G.; Papadakaki, M.; Chliaoutakis, J. Ordinal Logistic Regression as a Tool To Estimate the Risk of Escalating Outcomes. An Application to Vehicle Crash Data. Preprints 2023, 2023041023. https://doi.org/10.20944/preprints202304.1023.v1

Abstract

The use of logistic regression models in data analysis and machine learning has expanded in recent years and has become the primary preference of researchers in risk assessment studies across a wide range of scientific fields. From the assessment of credit risk in financial institutions to the estimation of risk factors for traffic accidents or the identification of etiological factors for chronic diseases. All logistic models are natural extensions of the simple binary model, and their interpretation is based on it. Using the data of a cross-sectional study on the risk factors of traffic collisions, the two main extended models of logistic techniques, multinomial and ordinal logistic regression, are presented in the article in detail. Emphasis is placed on the use of ordinal regression since the outcome variable of the collision data is defined as ordinal measurement reflecting a latent continuous scale.

Keywords

vehicle crash data; collision risk; ordinal logistic regression; multinomial logistic regression; proportional odds model (POM); partial proportional odds model (PPOM)

Subject

Social Sciences, Safety Research

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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