POSTERIOR CONSISTENCY IN CONDITIONAL DENSITY ESTIMATION BY COVARIATE DEPENDENT MIXTURES
By Andriy Norets, Justinas Pelenis. Econometric Theory, 2011
50 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/f9b6672d87a24e63349744d4c39374b8ec219727
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
- Alejandro Jara
- Fernando A. Quintana
- Andrés F. Barrientos
- Federico Bassetti
- Roberto Casarin
- Francesco Ravazzolo
- Sotirios P. Chatzis
- Athanasios Kottas
In Statistics and Probability
- Alejandro Jara
- Fernando A. Quintana
- Andrés F. Barrientos
- Federico Bassetti
- Roberto Casarin
- Francesco Ravazzolo
- Sotirios P. Chatzis
- Athanasios Kottas
Citations in the corpus
- Bayesian Nonparametric Calibration and Combination of Predictive Distributions (2015, background): “In this sense, we also contribute to the recent literature on posterior consistency of Bayesian nonparametric inference (e.g., see Tang and Ghosal 2007, Pelenis 2014 and Norets and Pelenis 2014).”
- On the Support of MacEachern’s Dependent Dirichlet Processes and Extensions (2012, methodology): “Some recent results have been provided by Pati et al. (2011) and Norets and Pelenis (2011), in the context of dependent mixtures of Gaussians induced by probit stick–breaking processes (Chung and Dunson 2009), and dependent mixtures of location–scale distributions induced by finite mixing…”