Zero‐Inflated Negative Binomial Mixed Regression Modeling of Over‐Dispersed Count Data with Extra Zeros

By Kelvin K. W. Yau, Kui Wang, Andy H. Lee. 2003

354 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4327b2a88b8d0796382956ff4cd76c26947ebb68

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. Emiliano A. Valdez
2. Katrien Antonio
3. Lluís Bermúdez
4. Dimitris Karlis

In Statistics and Probability

1. Emiliano A. Valdez
2. Kazem Mohammad
3. Abbas Moghimbeigi
4. Mohammed Reza Eshraghian
5. Katrien Antonio
6. Brian McArdle
7. Lluís Bermúdez
8. Dimitris Karlis

Citations in the corpus

• Statistical concepts of a priori and a posteriori risk classification in insurance (2011, background): “Cameron and Trivedi (1998), Winkelmann (2003), Yau et al. (2003), and Lee et al. (2006) similarly discuss regression modeling of data with excess zeros found in econometrics and medical statistics.”
• Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros (2008, methodology): “26 used single-level ZINB mixed regression to handle over-dispersion.”