Version 1
: Received: 20 February 2023 / Approved: 22 February 2023 / Online: 22 February 2023 (09:37:47 CET)
How to cite:
Untadi, A.; Li, L.D.; Li, M.; Dodd, R. Modelling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion. Preprints2023, 2023020383. https://doi.org/10.20944/preprints202302.0383.v1
Untadi, A.; Li, L.D.; Li, M.; Dodd, R. Modelling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion. Preprints 2023, 2023020383. https://doi.org/10.20944/preprints202302.0383.v1
Untadi, A.; Li, L.D.; Li, M.; Dodd, R. Modelling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion. Preprints2023, 2023020383. https://doi.org/10.20944/preprints202302.0383.v1
APA Style
Untadi, A., Li, L.D., Li, M., & Dodd, R. (2023). Modelling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion. Preprints. https://doi.org/10.20944/preprints202302.0383.v1
Chicago/Turabian Style
Untadi, A., Michael Li and Roland Dodd. 2023 "Modelling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion" Preprints. https://doi.org/10.20944/preprints202302.0383.v1
Abstract
Building fires are preventable incidents that have proven to be both deadly and costly. Addressing their root causes will lead to safer neighbourhoods for families and businesses to live and operate in. Multiple studies have established the effect of residents’ socioeconomic compositions on an area’s building fire rates; however, the existing model based on the classical stepwise regression procedure has several limitations. This paper aims to construct a more accurate predictive model of building fire rates based on a set of explanatory socioeconomic variables. In building the socioeconomic model, a backward elimination by Robust Final Predictor Error (RFPE) criterion is proposed to enhance the forecasting capability of the model. The proposed method has been implemented on the census data and the fire incident data of the South East Queensland region in Australia. A cross-validation was then conducted to assess the model’s accuracy. In addition, comparative analyses of other elimination criteria, such as p-value, adjusted R-squared, Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC) and predicted residual error sum of squared (PRESS), were conducted. The cross-validation analyses demonstrate that the proposed criterion is a more accurate predictive model based on a couple of goodness-of-fit measures. All in all, the RFPE equation was found to be a suitable criterion for the backward elimination procedure in the socioeconomic modelling of building fires.
Keywords
Building fire; Socioeconomic determinants; South East Queensland; Predictive model; Forecasting; Backward elimination; Robust final predictor error
Subject
Computer Science and Mathematics, Probability and Statistics
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.
