Analysis of Long-Tailed Count Data by Poisson Mixtures

By Ramesh C. Gupta, Seng Huat Ong. 2005

47 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4f9f844dc3de84bd24c3338f006f5763457c6c83

How researchers see this paper

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

In Artificial Intelligence

1. D. Lord
2. Yunlong Zhang
3. Yajie Zou

In Statistics and Probability

1. D. Lord
2. Yunlong Zhang
3. Yajie Zou

Citations in the corpus

• Modeling over-dispersed crash data with a long tail: Examining the accuracy of the dispersion parameter in Negative Binomial models (2015, background): “Previous studies (Stein et al., 1987; Gupta and Ong, 2005) have shown that the SI distribution is useful as a model for over-dispersed count data with a long tail.”
• Comparison of Sichel and Negative Binomial Models in Estimating Empirical Bayes Estimates (2013, background): “In addition, Gupta and Ong examined various mixed Poisson distributions for analyzing long-tailed count data and found that the SI model can provide a satisfactory fit in many cases where other models proved inadequate (17).”