Random parameters multivariate tobit and zero-inflated count data models: addressing unobserved and zero-state heterogeneity in accident injury-severity rate and frequency analysis

By Panagiotis Ch. Anastasopoulos. 2016

161 citations recorded by the source corpus.

Subfields: Public Health, Environmental and Occupational Health, Safety, Risk, Reliability and Quality, Transportation

View Paper: https://www.semanticscholar.org/paper/2f179bedcc2c98a45327d26cdfbaeb4fca2adde7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Public Health, Environmental and Occupational Health

1. Grigorios Fountas
2. Panagiotis Ch. Anastasopoulos
3. Xin Pei
4. Q. Zeng
5. Sze Chun Wong
6. Huiying Wen
7. Helai Huang
8. Salvador Hernández

In Safety, Risk, Reliability and Quality

1. Grigorios Fountas
2. Panagiotis Ch. Anastasopoulos
3. Xin Pei
4. Q. Zeng
5. Sze Chun Wong
6. Huiying Wen
7. Helai Huang
8. Ali Behnood

In Transportation

1. Grigorios Fountas
2. Xin Pei
3. Q. Zeng
4. Sze Chun Wong
5. Huiying Wen
6. Panagiotis Ch. Anastasopoulos

Citations in the corpus

• Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes (2018, unknown): “…applied in crash injury severity analyses (Russo et al., 2014; Zhao and Khattak, 2015, 2017, Behnood and Mannering, 2016, 2017a, 2017b; Naik et al., 2016; Anderson and Hernandez, 2017; Fountas and Anastasopoulos, 2017; Seraneeprakarn et al., 2017) and crash rate analyses (Anastasopoulos, 2016).”
• Analysis of accident injury-severity outcomes: The zero-inflated hierarchical ordered probit model with correlated disturbances (2018, background): “…the zero-inflated negative binomial models (Jang et al., 2010; Usman et al., 2010; Dong et al., 2014b; Anastasopoulos, 2016; Cai et al., 2016; Liu et al., 2018) as well as the (univariate or multivariate) tobit models (Anastasopoulos et al., 2012a, 2012b; Anastasopoulos, 2016; Zeng et al., 2017).”
• Jointly modeling area-level crash rates by severity: a Bayesian multivariate random-parameters spatio-temporal Tobit regression (2019, background): “Multivariate Tobit regression and its randomparameters version have been advocated for this purpose (Anastasopoulos et al. 2012; Anastasopoulos 2016; Zeng et al. 2017b).”
• Incorporating temporal correlation into a multivariate random parameters Tobit model for modeling crash rate by injury severity (2018, background): “However, only four studies (Anastasopoulos, Shankar, et al. 2012; Xu, Wong, and Choi 2014; Anastasopoulos 2016; Zeng et al., “AMultivariate Random-Parameters,” 2017) have focused on joint modeling crash rate by injury severity.”
• Estimation and Inference for Generalized Geoadditive Models (2020, unknown): “2016; Anastasopoulos 2016).”