Bayesian analysis for correlated Ordinal Data Models
By M. Chen, Dipak K. Dey. 2000
83 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/4debe039c6412928686cc2e634e522fabdb2aa03
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In Artificial Intelligence
- Chandra R. Bhat
- Andreas Brezger
- Winfried Steiner
- Yeongjin Gwon
- Fang Chen
- Maria DeYoreo
- Joseph G. Ibrahim
- Ming-Hui Chen
In Statistics and Probability
- Chandra R. Bhat
- Thomas Kneib
- Andreas Brezger
- Winfried Steiner
- Joseph G. Ibrahim
- Stefan Lang
- Nazneen Ferdous
- Cristiano Varin
Citations in the corpus
- Bayesian Nonparametric Modeling for Multivariate Ordinal Regression (2014, methodology): “ARTICLE HISTORY Received November Revised February KEYWORDS Dirichlet process mixture model; Kullback–Leibler condition; Markov chain Monte Carlo; Polychoric correlations”
- A comparison of the maximum simulated likelihood and composite marginal likelihood estimation approaches in the context of the multivariate ordered-response model (2010, methodology): “Earlier applications of the CML approach (and specifically the pairwise likelihood approach) to multivariate ordered-response systems include de Leon (2005) and Ferdous et al. (2010) in the context of cross-sectional multivariate ordered-response probit (CMOP) systems, and Varin and Vidoni (2006) an”
- The Power Prior: Theory and Applications (2015, background): “The power prior for variable subset selection was demonstrated in [12, 13, 31, 33, 40].”
- A New Algorithm for Simulating a Correlation Matrix Based on Parameter Expansion and Reparameterization (2006, background): “…is necessary, such as shrinkage models for normal regression (Barnard, McCulloch, and Meng 2001), scale mixtures of multivariate normal link models (Chen and Dey 1998, 2000) and joint models for longitudinal binary and continuous responses (Gueorguieva and Agresti 2001); for the final case, the…”
- Monotonic Regression Based on Bayesian P–Splines (2008, methodology): “In binary probit and cumulative probit models, we can rely on the same sampling scheme as building block, see Chen and Dey (2000) or Brezger and Lang (2003) for details.”
- Generalized structured additive regression based on Bayesian P-splines (2006, methodology): “in Chen and Dey (2000) or Fahrmeir and Lang (2001b). Recently, another important data augmentation approach for binary and multinomial logit models has been presented by Holmes and Held (2003).”