Flexible Parametric Models for Long-tailed Patent Count Distributions

By Jiemin Guo, Pravin K. Trivedi. 2002

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Developing a Random Parameters Negative Binomial-Lindley Model to analyze highly over-dispersed crash count data (2018, background): “Guo and Trivedi (2002) noted that a negligible probability is usually assigned to higher crash counts in the NB model during the modeling of highly over-dispersed data with a heavy tail.”
Modeling over-dispersed crash data with a long tail: Examining the accuracy of the dispersion parameter in Negative Binomial models (2015, background): “For example, Guo and Trivedi (2002) have reported that NB regression models have difficulties modeling heavily over-dispersed data with a long-tail and relatively high mean value because a negligible probability is usually assigned to high counts.”
Is there complementarity or substitutability between internal and external R&D strategies? (2012, background): “…as a major indicator of innovation, has later on been studied extensively (Blundell et al., 1995; Blundell et al., 2002; Crépon and Duguet, 1997; Guo and Trivedi, 2002; Montalvo, 1997), with the overall R&D elasticity of patents varying from 0.4 to 0.7 and thereby suggesting decreasing returns…”