Bayesian Analysis for the Zero-inflated Regression Models
By Hakjin Jang, Yun-Hee Kang, S. Lee, Seong W. Kim. 2008
119 citations recorded by the source corpus.
Subfields: Building and Construction, Pulmonary and Respiratory Medicine, Safety, Risk, Reliability and Quality
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Building and Construction
In Pulmonary and Respiratory Medicine
In Safety, Risk, Reliability and Quality
Citations in the corpus
- A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use (2010, methodology): “The advantages of the Bayesian approach include ease of interpretation, incorporation of prior information, practical estimation of any function of parameters or predictive values and reduced small-sample bias compared to maximum likelihood procedures (Ghosh et al., 2006).”
- WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods (2009, background): “2004; Wintle and Bardos 2006), imperfect detection (Royle and Dorazio 2006), heterogeneity in the detection process (Durban and Elston 2005), excess of zeros (Martin et al. 2005b; Ghosh et al. 2006), observer effects (Thogmartin et al.”
- Zero-inflated generalized Poisson models with regression effects on the mean, dispersion and zero-inflation level applied to patent outsourcing rates (2007, unknown): “Here we would like to mention a paper by Ghosh et al. (2006) which considers an alternative flexible class of zeroinflated regression models associated with power series distributions.”
- Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh (2008, methodology): “Similar expansions hold even under contiguous alternatives with non-central chi-squares replacing the chi-square leading term as shown in [14].”