Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data

By Prasad Buddhavarapu, James G. Scott, J. Prozzi. 2016

59 citations recorded by the source corpus.

Subfields: Epidemiology, Safety, Risk, Reliability and Quality, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c113484f4c156c33b2dc2a170d19999f1d5c2368

How researchers see this paper

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

In Safety, Risk, Reliability and Quality

1. S. Geedipally
2. Mohammad Razaur Rahman Shaon
3. Xiao Qin
4. Mohammadali Shirazi
5. D. Lord
6. Yunlong Zhang

In Statistics and Probability

1. S. Geedipally
2. D. Lord

Citations in the corpus

• Developing a Random Parameters Negative Binomial-Lindley Model to analyze highly over-dispersed crash count data (2018, methodology): “Buddhavarapu et al. (2016) developed a spatial finite-mixture RPNB model that relaxed the distributional assumptions of RP.”