Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model
LY Chang, JT Chien - Safety science, 2013 - Elsevier
To explore the factors contributing to driver injury severity in traffic accidents, parametric
regression models, such as multinomial logit models (MNLs) or ordered probabilistic
regression models, have been commonly applied for many years. However, these
parametric models have their own model assumptions and pre-defined underlying
relationships between dependent and independent variables. If these assumptions are
violated, the models can lead to erroneous estimation of the injury likelihood. This study …
regression models, such as multinomial logit models (MNLs) or ordered probabilistic
regression models, have been commonly applied for many years. However, these
parametric models have their own model assumptions and pre-defined underlying
relationships between dependent and independent variables. If these assumptions are
violated, the models can lead to erroneous estimation of the injury likelihood. This study …
Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model.
CLY Chang LiYen, CJT Chien JuiTseng - 2012 - cabidigitallibrary.org
To explore the factors contributing to driver injury severity in traffic accidents, parametric
regression models, such as multinomial logit models (MNLs) or ordered probabilistic
regression models, have been commonly applied for many years. However, these
parametric models have their own model assumptions and pre-defined underlying
relationships between dependent and independent variables. If these assumptions are
violated, the models can lead to erroneous estimation of the injury likelihood. This study …
regression models, such as multinomial logit models (MNLs) or ordered probabilistic
regression models, have been commonly applied for many years. However, these
parametric models have their own model assumptions and pre-defined underlying
relationships between dependent and independent variables. If these assumptions are
violated, the models can lead to erroneous estimation of the injury likelihood. This study …
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