Nonparametric Bayesian Modeling for Multivariate Ordinal Data
By Athanasios Kottas, Peter Müller, Fernando A. Quintana. 2005
119 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/30742754c57e547679664b3a4025bb31924f2da4
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
- Athanasios Kottas
- S. MacEachern
- David B. Dunson
- Maria DeYoreo
- Hikaru Hasegawa
- Gary Rosner
- Peter Müller
- Matt Taddy
In Statistics and Probability
- Athanasios Kottas
- S. MacEachern
- Maria DeYoreo
- David B. Dunson
- Hikaru Hasegawa
- Gary Rosner
- Peter Müller
- Maria De Iorio
Citations in the corpus
- An ANOVA Model for Dependent Random Measures (2004, methodology): “Kottas, Müller, and Quintana (2002) showed that without loss of generality the cutoffs µj can be xed when using a nonparametric prior model for the latent variables yi .”
- Bayesian Nonparametric Modeling for Multivariate Ordinal Regression (2014, methodology): “The latter modeling approach is an extension of the work in Kottas, Müller, and Quintana (2005), where, in the context of multivariate ordinal data without covariates, the latent response distribution was modeled with a DP mixture of multivariate normals.”
- Analyzing tourists' satisfaction: A multivariate ordered probit approach (2010, background): “Numerous studies have been devoted to analyzing tourist satisfaction; for example, Kozak (2001b), Yuksel and Yuksel (2001a,b), Bowen and Clarke (2002), and Ryan and Cessford (2003) review the research on tourist satisfaction.”