Analyzing different functional forms of the varying weight parameter for finite mixture of negative binomial regression models

60 citations recorded by the source corpus.

How researchers see this paper

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

Random parameters multivariate tobit and zero-inflated count data models: addressing unobserved and zero-state heterogeneity in accident injury-severity rate and frequency analysis (2016, background): “67 In fact, numerous studies (Abdel-Aty and Radwan, 2000; Anastasopoulos et al., 2008; Geedipally and Lord, 68 2010; Mohammadi et al., 2014; Zou et al., 2014; Barua et al., 2015; for a detailed list, see Lord and 69 Mannering, 2010) have presented a great amount of roadway segments of varying…”
Modeling over-dispersed crash data with a long tail: Examining the accuracy of the dispersion parameter in Negative Binomial models (2015, methodology): “Besides the mixed-Poisson models, the random parameters count models (Anastasopoulos and Mannering, 2009; Chen and Tarko, 2014), finite mixture and Markov switching models (Park and Lord, 2009; Malyshkina et al., 2009; Zou et al., 2013b; Zou et al., 2014), generalized ordered-response models (Castro”